Minicursos do XXII Simpósio Brasileiro de Computação Aplicada à Saúde

A. Alqahtani. Application of artificial intelligence in discovery and development of anticancer and antidiabetic therapeutic agents. Evid Based Complement Alternat Med, 2022:6201067, 2022.

A. Angelis, A. Lange, and P. Kanavos. Using health technology assessment to assess the value of new medicines: results of a systematic review and expert consultation across eight european countries. Eur J Health Econ, 19(1):123–152, 2018.

A. E. Nicogossian, D. F. Pober, and S. A. Roy. Evolution of telemedicine in the space program and earth applications. Telemed J E Health, 7(1):1–15, 2001.

A. H. Mayer, V. F. Rodrigues, C. A. d. Costa, R. d. R. Righi, A. Roehrs, and R. S. Antunes. Fogchain: A fog computing architecture integrating blockchain and internet of things for personal health records. IEEE Access, 9:122723–122737, 2021.

A. Hosny, C. Parmar, J. Quackenbush, L. H. Schwartz, and Hjwl Aerts. Artificial intelligence in radiology. Nat Rev Cancer, 18(8):500–510, 2018.

A. I. Consort and Spirit-Ai Steering Group. Reporting guidelines for clinical trials evaluating artificial intelligence interventions are needed. Nat Med, 25(10):1467– 1468, 2019.

A. Labrique, S. Agarwal, T. Tamrat, and G. Mehl. Who digital health guidelines: a milestone for global health. NPJ Digit Med, 3:120, 2020.

A. Roehrs, C. A. da Costa, R. R. Righi, A. H. Mayer, V. F. da Silva, J. R. Goldim, and D. C. Schmidt. Integrating multiple blockchains to support distributed personal health records. Health Informatics J, 27(2):14604582211007546, 2021.

Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., and Zheng, X. (2015). TensorFlow: Large-scale machine learning on heterogeneous systems. Software available from https://tensorflow.org.

Abhishek Hazra, Mainak Adhikari, Tarachand Amgoth, and Satish Narayana Srirama. A comprehensive survey on interoperability for iiot: Taxonomy, standards, and future directions. ACM Computing Surveys (CSUR), 55(1):1–35, 2021.

Abrahão M T; Nobre, M R C; Madril, P J; O estado da arte em pesquisa observacional de dados de saúde: A iniciativa OHDSI. In: Artur Ziviani; Natalia Castro Fernandes; Débora Christina Muchaluat Saade. (Org.). Livro de Minicursos do 19o Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS 2019). 19o ed. Porto Alegre: Sociedade Brasileira de Computação-SBC, (2019), I SBN-13 (15) 978-85-7669-472-4, v. 1, p. 141-189.

Agrawal, R. and Srikant, R. (1995). Mining sequential patterns. In Proceedings of the eleventh international conference on data engineering, IEEE.

Agrawal, R., Srikant, R., et al. (1994). Fast algorithms for mining association rules. In Proc. 20th int. conf. very large data bases, VLDB, volume 1215, pages 487–499. Citeseer.

Ah Ra Lee, Il Kon Kim, and Eunjoo Lee. Developing a transnational health record framework with level-specific interoperability guidelines based on a related literature review. In Healthcare, volume 9, page 67. Multidisciplinary Digital Publishing Institute, 2021.

Ahmed Slalmi, Hasna Chaibi, Abdellah Chehri, Rachid Saadane, and Gwanggil Jeon. Toward 6g: Understanding network requirements and key performance indicators. Transactions on Emerging Telecommunications Technologies, 32(3):e4201, 2021.

AHNA - American Holistic Nurses Association. (2020) “What is Holistic Nursing”, https://www.ahna.org/Home/Publications.

AJ Kalil, VM de CH Dias, C da C Rocha, HMP Morales, JL Fressatto, and RA de Faria. Sepsis risk assessment: a retrospective analysis after a cognitive risk management robot (robot laura®) implementation in a clinical-surgical unit. Research on Biomedical Engineering [online], 34(4):6, 2018.

Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25, 2012.

Alex Roehrs, Cristiano André Da Costa, and Rodrigo da Rosa Righi. Omniphr: A distributed architecture model to integrate personal health records. Journal of biomedical informatics, 71:70–81, 2017.

Alex Roehrs, Cristiano André da Costa, Rodrigo da Rosa Righi, Sandro José Rigo, and Matheus Henrique Wichman. Toward a model for personal health record interoperability. IEEE journal of biomedical and health informatics, 23(2):867–873, 2018.

Alex Roehrs, Cristiano André da Costa, Rodrigo da Rosa Righi, Valter Ferreira da Silva, José Roberto Goldim, and Douglas C. Schmidt. Analyzing the performance of a blockchain-based personal health record implementation. Journal of Biomedical Informatics, 92:103140, 2019.

Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2021.

Ali Bou Nassif, Ismail Shahin, Imtinan Attili, Mohammad Azzeh, and Khaled Shaalan. Speech recognition using deep neural networks: A systematic review. IEEE access, 7:19143–19165, 2019.

Alom, M. Z., Hasan, M., Yakopcic, C., Taha, T. M., and Asari, V. K. (2018). Recurrent residual convolutional neural network based on u-net (r2u-net) for medical image segmentation. CoRR, abs/1802.06955.

Amanatiadis, A., Kaburlasos, V. G., Dardani, C., and Chatzichristofis, S. A. (2017). Interactive social robots in special education. In 2017 IEEE 7th international conference on consumer electronics-Berlin (ICCE-Berlin), pages 126–129. IEEE.

Amir M. Rahmani, Tuan Nguyen Gia, Behailu Negash, Arman Anzanpour, Iman Azimi, Mingzhe Jiang, and Pasi Liljeberg. Exploiting smart e-health gateways at the edge of healthcare internet-of-things: A fog computing approach. Future Generation Computer Systems, 78:641–658, 2018.

Amit P Sheth. Changing focus on interoperability in information systems: from system, syntax, structure to semantics. In Interoperating geographic information systems, pages 5–29. Springer, 1999.

Anderson, R.J. (2011) “Florence Nightingale: The Biostatistician”, Molecular Interventions, http://dx.doi.org/10.1124/mi.11.2.1.

André Henrique Mayer, Cristiano André da Costa, and Rodrigo da Rosa Righi. Electronic health records in a blockchain: A systematic review. 26(2):1273–1288, 2020.

Andrews, R., Wynn, M. T., Vallmuur, K., Ter Hofstede, A. H., and Bosley, E. (2020). A comparative process mining analysis of road trauma patient pathways. International Journal of Environmental Research and Public Health, 17(10).

Ang, E., Kwasnick, S., Bayati, M., Plambeck, E. L., and Aratow, M. (2016). Accurate emergency department wait time prediction. Manuf. Serv. Oper. Manag., 18(1):141–156.

Annaz, D., Karmiloff-Smith, A., Johnson, M. H., and Thomas, M. S. (2009). A cross-syndrome study of the development of holistic face recognition in children with autism, down syndrome, and williams syndrome. Journal of experimental child psychology, 102(4):456–486.

Anthony D. Harries, Rony Zachariah, Anil Kapur, Andreas Jahn, and Donald A. Enarson. The vital signs of chronic disease management. Transactions of The Royal Society of Tropical Medicine and Hygiene, 103(6):537–540, 06 2009.

Antonelli, D., Baralis, E., Bruno, G., Chiusano, S., Mahoto, N. A., and Petrigni, C. (2012). Analysis of diagnostic pathways for colon cancer. Flexible Services and Manufacturing Journal, 24(4):379–399.

Ardakani, A. A., Kanafi, A. R., Acharya, U. R., Khadem, N., and Mohammadi, A. (2020). Application of deep learning technique to manage covid-19 in routine clinical practice using ct images: Results of 10 convolutional neural networks. Computers in Biology and Medicine, 121:103795.

Arias, M., Rojas, E., Aguirre, S., Cornejo, F., Munoz-Gama, J., Sepúlveda, M., and Capurro, D. (2020). Mapping the patient’s journey in healthcare through process mining. International Journal of Environmental Research and Public Health, 17(18):1–16.

Arnolds, I. V. and Gartner, D. (2018). Improving hospital layout planning through clinical pathway mining. Annals of Operations Research, 263(1-2):453–477.

Aron K Barbey. Network neuroscience theory of human intelligence. Trends in cognitive sciences, 22(1):8–20, 2018.

Aspland, E., Harper, P. R., Gartner, D., Webb, P., and Barrett-Lee, P. (2021). Modified Needleman–Wunsch algorithm for clinical pathway clustering. Journal of Biomedical Informatics, 115.

Attawibulkul, S., Sornsuwonrangsee, N., Jutharee, W., and Kaewkamnerdpong, B. (2019). Using storytelling robot for supporting autistic children in theory of mind. International Journal of Bioscience, Biochemistry and Bioinformatics, 9(2):100–108.

Austin, R. Monsen, K. Alexander, S. (2021) “Capturing Whole-Person Health Data Using Mobile Applications”, Clin. Nurse Spec, [link].

Axiaq, A., Almohtadi, A., Massias, S. A., Ngemoh, D., and Harky, A. (2021). The role of computed tomography scan in the diagnosis of covid-19 pneumonia. Current Opinion in Pulmonary Medicine, 27(3).

Azuar, D., Gallud, G., Escalona, F., Gomez-Donoso, F., and Cazorla, M. (2019). A story-telling social robot with emotion recognition capabilities for the intellectually challenged. In Iberian Robotics conference, pages 599–609. Springer.

Baker, K., Dunwoodie, E., Jones, R. G., Newsham, A., Johnson, O., Price, C. P.,Wolstenholme, J., Leal, J., McGinley, P., Twelves, C., and Hall, G. (2017). Process mining routinely collected electronic health records to define real-life clinical pathways during chemotherapy. International Journal of Medical Informatics, 103:32– 41.

Banda, J. M., Y. Halpern, D. Sontag, and N. H. Shah. 2017. “Electronic phenotyping with APHRODITE and the Observational Health Sciences and Informatics (OHDSI) data network.” AMIA Jt Summits Transl Sci Proc (2017): 48–57.

Baptiste Vasey, Myura Nagendran, Bruce Campbell, David A. Clifton, Gary S. Collins, Spiros Denaxas, Alastair K. Denniston, Livia Faes, Bart Geerts, Mudathir Ibrahim, Xiaoxuan Liu, Bilal A. Mateen, Piyush Mathur, Melissa D. McCradden, Lauren Morgan, Johan Ordish, Campbell Rogers, Suchi Saria, Daniel S. W. Ting, Peter Watkinson, Wim Weber, Peter Wheatstone, Peter McCulloch, and Decide- A. I. expert group the. Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: Decide-ai. Nature Medicine, 28(5):924–933, 2022.

Barakova, E. and Lourens, T. (2013). Interplay between natural and artificial intelligence in training autistic children with robots. In International Work-Conference on the Interplay Between Natural and Artificial Computation, pages 161–170. Springer.

Baro, E., Degoul, S., Beuscart, R., and Chazard, E. (2015). Toward a literature-driven definition of big data in healthcare. BioMed research international, 2015.

Bartl-Pokorny, K. D., Pykała, M., Uluer, P., Barkana, D. E., Baird, A., Kose, H., Zorcec, T., Robins, B., Schuller, B.W., and Landowska, A. (2021). Robot-based intervention for children with autism spectrum disorder: a systematic literature review. IEEE Access.

Basole, R. C., Braunstein, M. L., Kumar, V., Park, H., Kahng, M., Chau, D. H., Tamersoy, A., Hirsh, D. A., Serban, N., Bost, J., Lesnick, B., Schissel, B. L., and Thompson, M. (2015). Understanding variations in pediatric asthma care processes in the emergency department using visual analytics. Journal of the American Medical Informatics Association, 22(2):318–323.

Benevento, E., Aloini, D., Squicciarini, N., Dulmin, R., and Mininno, V. (2019). Queue-based features for dynamic waiting time prediction in emergency department. Measuring Business Excellence, 23(4):458–471.

Bengio, Y., Courville, A., and Vincent, P. (2013). Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8):1798–1828.

Berndt, D. J. and Clifford, J. (1994). Using dynamic time warping to find patterns in time series. In Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining, AAAIWS’94, page 359–370. AAAI Press.

Berwick, D.M. (2020) “The moral determinants of health”, JAMA, https://jamanetwork.com/journals/jama/fullarticle/2767353.

Bettencourt-Silva, J. H., Clark, J., Cooper, C. S., Mills, R., Rayward-Smith, V. J., and Iglesia, B. D. L. (2015). Building Data-Driven Pathways from Routinely Collected Hospital Data: A Case Study on Prostate Cancer. JMIR Medical Informatics, 3(3).

Bhavneet Bhinder, Coryandar Gilvary, Neel S. Madhukar, and Olivier Elemento. Artificial intelligence in cancer research and precision medicine. Cancer discovery, 11(4):900–915, 2021.

Blei, D. M., Ng, A. Y., and Jordan, M. I. (2003). Latent dirichlet allocation. J. Mach. Learn. Res., 3(null):993–1022.

Bose, E. et al. (2019) “Machine Learning Methods for Identifying Critical Data Elements in Nursing Documentation”, Nursing Research, [link].

Bradski, G. (2000). The OpenCV Library. Dr. Dobb’s Journal of Software Tools.

C. Chen, EW. Loh, K.N. Kuo, and KW Tam. The times they are a-changin’ – healthcare 4.0 is coming! Systems-Level Quality Improvement, 44(40), 2020.

Çiçek, Ö., Abdulkadir, A., Lienkamp, S. S., Brox, T., and Ronneberger, O. (2016). 3d u-net: Learning dense volumetric segmentation from sparse annotation. In Ourselin, S., Joskowicz, L., Sabuncu, M. R., Unal, G., andWells, W., editors, Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016, pages 424–432, Cham. Springer International Publishing.

Cabibihan, J.-J., Javed, H., Ang, M., and Aljunied, S. M. (2013). Why robots? a survey on the roles and benefits of social robots in the therapy of children with autism. International journal of social robotics, 5(4):593–618.

Calderia, S. and Timmins, F. (2016) “Resilience: synthesis of concept analyses and contribution to nursing classifications”, International Nursing Review, https://doi.org/10.1111/inr.12268.

Campbell-Sills, L. and Stein, M.B. (2007) “Psychometric analysis and refinement of the Connor-Davidson Resilience Scale (CD-RISC): Validation of a 10-item measure of resilience”, J. Trauma. Stress, https://doi.org/10.1002/jts.20271.

Cano, S., González, C. S., Gil-Iranzo, R. M., and Albiol-Pérez, S. (2021). Affective communication for socially assistive robots (sars) for children with autism spectrum disorder: A systematic review. Sensors, 21(15):5166.

Cao Xiao, Edward Choi, and Jimeng Sun. Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review. Journal of the American Medical Informatics Association, 25(10):1419– 1428, 2018.

Cao, H.-L., Pop, C., Simut, R., Furnemónt, R., De Beir, A., Van de Perre, G., Esteban, P. G., Lefeber, D., and Vanderborght, B. (2015). Probolino: A portable low-cost social device for home-based autism therapy. In International Conference on Social Robotics, pages 93–102. Springer.

Caron, F., Vanthienen, J., Vanhaecht, K., Limbergen, E. V., De Weerdt, J., and Baesens, B. (2014). Monitoring care processes in the gynecologic oncology department. Computers in Biology and Medicine, 44(1):88–96.

Cassell, J., Pelachaud, C., Badler, N., Steedman, M., Achorn, B., Becket, T., Douville, B., Prevost, S., and Stone, M. (1994). Animated conversation: rule-based generation of facial expression, gesture & spoken intonation for multiple conversational agents. In Proceedings of the 21st annual conference on Computer graphics and interactive techniques, pages 413–420.

Castiglione, A., Vijayakumar, P., Nappi, M., Sadiq, S., and Umer, M. (2021). Covid-19: Automatic detection of the novel coronavirus disease from ct images using an optimized convolutional neural network. IEEE Transactions on Industrial Informatics, 17(9):6480–6488.

Chao Yu, Jiming Liu, Shamim Nemati, and Guosheng Yin. Reinforcement learning in healthcare: A survey. ACM Computing Surveys (CSUR), 55(1):1–36, 2021.

Chayakrit Krittanawong, Albert J. Rogers, Mehmet Aydar, Edward Choi, Kipp W. Johnson, Zhen Wang, and Sanjiv M. Narayan. Integrating blockchain technology with artificial intelligence for cardiovascular medicine. Nature Reviews Cardiology, 17(1):1–3, 2020.

Chen, H., Park, H.W., and Breazeal, C. (2020). Teaching and learning with children: Impact of reciprocal peer learning with a social robot on children’s learning and emotive engagement. Computers & Education, 150:103836.

Chen, J., Sun, L., Guo, C., Wei, W., and Xie, Y. (2018). A datadriven framework of typical treatment process extraction and evaluation. Journal of Biomedical Informatics, 83:178–195.

Chiudinelli, L., Dagliati, A., Tibollo, V., Albasini, S., Geifman, N., Peek, N., Holmes, J. H., Corsi, F., Bellazzi, R., and Sacchi, L. (2020). Mining postsurgical care processes in breast cancer patients. Artificial Intelligence in Medicine, 105.

Cho, M., Kim, K., Lim, J., Baek, H., Kim, S., Hwang, H., Song, M., and Yoo, S. (2020). Developing data-driven clinical pathways using electronic health records: The cases of total laparoscopic hysterectomy and rotator cuff tears. International Journal of Medical Informatics, 133.

Christian Janiesch, Patrick Zschech, and Kai Heinrich. Machine learning and deep learning. Electronic Markets, 31(3):685–695, 2021.

Claire Arnaud, Thomas Bochaton, Jean-Louis Pépin, and Elise Belaidi. Obstructive sleep apnoea and cardiovascular consequences: pathophysiological mechanisms. Archives of cardiovascular diseases, 113(5):350–358, 2020.

Clifford, G. D. (2006). Shortliffe edward h, cimino james j: ”biomedical informatics; computer applications in health care and biomedicine”. BioMedical Engineering OnLine, 5(1):61.

Cohen, J. P., Morrison, P., Dao, L., Roth, K., Duong, T. Q., and Ghassemi, M. (2020). Covid-19 image data collection: Prospective predictions are the future.

Conca, T., Saint-Pierre, C., Herskovic, V., Sepúlveda, M., Capurro, D., Prieto, F., and Fernandez-Llatas, C. (2018). Multidisciplinary collaboration in the treatment of patients with type 2 diabetes in primary care: Analysis using process mining. Journal of Medical Internet Research, 20(4).

Connelly, L. G. and Bair, A. E. (2004). Discrete event simulation of emergency department activity: A platform for system-level operations research. Academic Emergency Medicine, 11(11):1177–1185. Cited By :159.

Costa, A. P., Steffgen, G., Lera, F. R., Nazarikhorram, A., and Ziafati, P. (2017). Socially assistive robots for teaching emotional abilities to children with autism spectrum disorder. In 3rd Workshop on Child-Robot Interaction at HRI.

Cruz Sandoval, D. (2020). Robot conversacional como apoyo a intervenciones no farmacológicas para adultos mayores con demencia Conversational robot to support non-pharmacological interventions for people with dementia. Tesis de doctorado en ciencias, Centro de Investigación Científica y de Educación Superior de Ensenada, Baja California. 125pp.

Cruz-Sandoval, D. and Favela, J. (2019). A Conversational Robot to Conduct Therapeutic Interventions for Dementia. IEEE Pervasive Computing, 18(2):10–19.

Cruz-Sandoval, D., Morales-Tellez, A., Sandoval, E. B., and Favela, J. (2020). A social robot as therapy facilitator in interventions to deal with dementia-related behavioral symptoms. In 2020 15th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pages 161–169. IEEE.

D Dowding, R Randell, P Gardner, G Fitzpatrick, P Dykes, J Favela, S Hamer, Z Whitewood-Moores, N Hardiker, E Borycki, and L Currie. Dashboards for improving patient care: Review of the literature. International Journal of Medical Informatics, 84:14, 2015.

D. C. Malta, R. P. F. Gonçalves, E. Machado Í, M. I. F. Freitas, C. Azeredo, and C. L. Szwarcwald. Prevalence of arterial hypertension according to different diagnostic criteria, national health survey. Rev Bras Epidemiol, 21(suppl 1):e180021, 2018.

D. Zeevi, T. Korem, N. Zmora, D. Israeli, D. Rothschild, A. Weinberger, O. Ben-Yacov, D. Lador, T. Avnit-Sagi, M. Lotan-Pompan, J. Suez, J. A. Mahdi, E. Matot, G. Malka, N. Kosower, M. Rein, G. Zilberman-Schapira, L. Dohnalová, M. Pevsner-Fischer, R. Bikovsky, Z. Halpern, E. Elinav, and E. Segal. Personalized nutrition by prediction of glycemic responses. Cell, 163(5):1079–1094, 2015.

Dagliati, A., Sacchi, L., Zambelli, A., Tibollo, V., Pavesi, L., Holmes, J. H., and Bellazzi, R. (2017). Temporal electronic phenotyping by mining careflows of breast cancer patients. Journal of Biomedical Informatics, 66:136–147.

Dagliati, A., Tibollo, V., Cogni, G., Chiovato, L., Bellazzi, R., and Sacchi, L. (2018). Careflow Mining Techniques to Explore Type 2 Diabetes Evolution. Journal of Diabetes Science and Technology, 12(2):251–259.

Dahlem, D., Maniloff, D., and Ratti, C. (2015). Predictability bounds of electronic health records. Scientific Reports, 5:1–9.

Dahlin, S. and Raharjo, H. (2019). Relationship between patient costs and patient pathways. International Journal of Health Care Quality Assurance, 32(1):246–261.

Dananché, C., Elias, C., Hénaff, L., Amour, S., Kuczewski, E., Gustin, M.-P., Escuret, V., Saadatian-Elahi, M., and Vanhems, P. (2022). Baseline clinical features of COVID-19 patients, delay of hospital admission and clinical outcome: A complex relationship. PLoS One, 17(1):e0261428.

Daniel W Otter, Julian R Medina, and Jugal K Kalita. A survey of the usages of deep learning for natural language processing. IEEE Transactions on Neural Networks and Learning Systems, 32(2):604–624, 2020.

de Carvalho, L. S., da Silva Júnior, R. T., Oliveira, B. V. S., de Miranda, Y. S., Rebouças, N. L. F., Loureiro, M. S., Pinheiro, S. L. R., da Silva, R. S., Correia, P. V. S. L. M., Silva, M. J. S., Ribeiro, S. N., da Silva, F. A. F., de Brito, B. B., Santos, M. L. C., Leal, R. A. O. S., Oliveira, M. V., and de Melo, F. F. (2021). Highlighting COVID-19: What the imaging exams show about the disease. World J. Radiol., 13(5):122–136.

De Oliveira, H., Augusto, V., Jouaneton, B., Lamarsalle, L., Prodel, M., and Xie, X. (2020). Automatic and explainable labeling of medical event logs with autoencoding. IEEE Journal of Biomedical and Health Informatics, 24(11):3076–3084.

de Oliveira, H., Augusto, V., Jouaneton, B., Lamarsalle, L., Prodel, M., and Xie, X. (2020a). Optimal process mining of timed event logs. Information Sciences, 528:58–78.

de Oliveira, H., Prodel, M., Lamarsalle, L., Inada-Kim, M., Ajayi, K., Wilkins, J., Sekelj, S., Beecroft, S., Snow, S., Slater, R., and Orlowski, A. (2020b). “Bow-tie” optimal pathway discovery analysis of sepsis hospital admissions using the Hospital Episode Statistics database in England. JAMIA Open, 3(3):439–448.

Defossez, G., Rollet, A., Dameron, O., and Ingrand, P. (2014). Temporal representation of care trajectories of cancer patients using data from a regional information system: An application in breast cancer. BMC Medical Informatics and Decision Making, 14(1):24.

Delaney, C.W. and Weaver, C. (2019). “The 7th nursing knowledge: Big data conference brings remarkable accomplishments and shows staying power on key fronts”, Computers, Informatics, Nursing, [link].

Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L. (2009). Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248–255. Ieee.

Dickstein-Fischer, L. A., Crone-Todd, D. E., Chapman, I. M., Fathima, A. T., and Fischer, G. S. (2018). Socially assistive robots: current status and future prospects for autism interventions. Innovation and Entrepreneurship in Health, 5:15–25.

Ding, R., McCarthy, M. L., Desmond, J. S., Lee, J. S., Aronsky, D., and Zeger, S. L. (2010). Characterizing waiting room time, treatment time, and boarding time in the emergency department using quantile regression. Academic Emergency Medicine, 17(8):813–823.

Dingwen Zhang, Junwei Han, Gong Cheng, and Ming-Hsuan Yang. Weakly supervised object localization and detection: a survey. IEEE transactions on pattern analysis and machine intelligence, 2021.

Djulbegovic, B. and Guyatt, G. H. (2017). Progress in evidence-based medicine: a quarter century on. The lancet, 390(10092):415–423.

Dong, J., Yom-Tov, G., and Yom-Tov, E. (2018). The impact of delay announcements on hospital network coordination and waiting times. Management Science.

Duguay, C. and Chetouane, F. (2007). Modeling and improving emergency department systems using discrete event simulation. Simulation, 83(4):311–320. Cited By :192.

Duma, D. and Aringhieri, R. (2020). An ad hoc process mining approach to discover patient paths of an Emergency Department. Flexible Services and Manufacturing Journal, 32(1):6–34.

Duquette, A., Michaud, F., and Mercier, H. (2008). Exploring the use of a mobile robot as an imitation agent with children with low-functioning autism. Autonomous Robots, 24(2):147–157.

Durojaiye, A. B., McGeorge, N. M., Puett, L. L., Stewart, D., Fackler, J. C., Hoonakker, P. L., Lehmann, H. P., and Gurses, A. P. (2018). Mapping the Flow of Pediatric Trauma Patients Using Process Mining. Applied Clinical Informatics, 9(3):654–666.

E. Shuster. Fifty years later: the significance of the nuremberg code. N Engl J Med, 337(20):1436–40, 1997.

Egho, E., Jay, N., Raïssi, C., Ienco, D., Poncelet, P., Teisseire, M., and Napoli, A. (2014). A contribution to the discovery of multidimensional patterns in healthcare trajectories. Journal of Intelligent Information Systems, 42(2):283–305.

Egho, E., Raïssi, C., Calders, T., Jay, N., and Napoli, A. (2015). On measuring similarity for sequences of itemsets. Data Mining and Knowledge Discovery, 29(3):732–764.

Elder, L. M., Caterino, L. C., Chao, J., Shacknai, D., and De Simone, G. (2006). The efficacy of social skills treatment for children with asperger syndrome. Education and Treatment of Children, pages 635–663.

Elliot Mbunge, Benhildah Muchemwa, Sipho’esihle Jiyane, and John Batani. Sensors and healthcare 5.0: transformative shift in virtual care through emerging digital health technologies. Global Health Journal, 5(4):169–177, 2021.

Elsa Negro-Calduch, Natasha Azzopardi-Muscat, Ramesh S Krishnamurthy, and David Novillo-Ortiz. Technological progress in electronic health record system optimization: Systematic review of systematic literature reviews. International journal of medical informatics, 152:104507, 2021.

Erden, M. S. (2013). Emotional postures for the humanoid-robot nao. International Journal of Social Robotics, 5(4):441–456.

Erdogan, T. G. and Tarhan, A. (2018). A goal-driven evaluation method based on process mining for healthcare processes. Applied Sciences (Switzerland), 8(6).

Ester, M., Kriegel, H.-P., Sander, J., and Xu, X. (1996). A densitybased algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, KDD’96, page 226–231. AAAI Press.

Ethan R Ellis and Mark E Josephson. Heart failure and tachycardia-induced cardiomyopathy. Current heart failure reports, 10(4):296–306, 2013.

European Commission and Directorate-General for Informatics. New European interoperability framework : promoting seamless services and data flows for European public administrations. Publications Office, 2017.

Fasola, J. and Mataric, M. J. (2012). Using socially assistive human–robot interaction to motivate physical exercise for older adults. Proceedings of the IEEE, 100(8):2512–2526.

Fei, H. and Meskens, N. (2013). Clustering of Patients’ Trajectories with an Auto-Stopped Bisecting K-Medoids Algorithm. Journal of Mathematical Modelling and Algorithms, 12(2):135–154.

Feil-Seifer, D. and Mataric, M. J. (2005). Defining socially assistive robotics. In 9th International Conference on Rehabilitation Robotics, 2005. ICORR 2005., pages 465–468. IEEE.

Fernandez, C. and Benedí, J. M. (2008). Timed Parallel Automaton learning inWorkflow Mining problems. In 1er. Congreso Internacional de Mecatrónica y 2do. Congreso Nacional UP, pages 1–8, Tuxtla Gutiérrez, Mexico.

Fernando Martin-Sanchez and Karin Verspoor. Big data in medicine is driving big changes. Yearbook of medical informatics, 23(01):14–20, 2014.

Ferreira, V.R. e Santana, A.G. (2021) “A quarta revolução industrial e o direito à desconexão do trabalhador em tempos de pandemia”, Cad. PPG Direito/UFRGS, https://www.seer.ufrgs.br/ppgdir/article/view/104993.

Fong, T., Nourbakhsh, I., and Dautenhahn, K. (2003). A survey of socially interactive robots. Robotics and autonomous systems, 42(3-4):143–166.

Forestier, G., Lalys, F., Riffaud, L., Trelhu, B., and Jannin, P. (2012). Classification of surgical processes using dynamic time warping. Journal of Biomedical Informatics, 45(2):255–264.

Francois Chollet. Deep learning with Python. Simon and Schuster, 2021.

François Chollet. Xception: Deep learning with depthwise separable convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1251–1258, 2017.

Frey, B. J. and Dueck, D. (2007). Clustering by passing messages between data points. Science, 315(5814):972–976.

Garg, L., McClean, S., Meenan, B., and Millard, P. (2009). Nonhomogeneous Markov models for sequential pattern mining of healthcare data. IMA Journal of Management Mathematics, 20(4):327–344.

Gonzalez-Garcia, J., Telleria-Orriols, C., Estupinan-Romero, F., and Bernal-Delgado, E. (2020). Construction of Empirical Care Pathways Process Models from Multiple Real-World Datasets. IEEE Journal of Biomedical and Health Informatics, 24(9):2671–2680.

Gratch, J., Okhmatovskaia, A., Lamothe, F., Marsella, S., Morales, M., van der Werf, R. J., and Morency, L.-P. (2006). Virtual rapport. In International Workshop on Intelligent Virtual Agents, pages 14–27. Springer.

GS Silveira, PR Ferreira, DS Silveira, and FCV Siqueira. Prevalence of absenteeism in medical appointments in a basic health unit in the south of brazil. Rev Bras Med Fam Comunidade, 13:7, 2018.

Gudlin, M., Ivanković, I., and Dadić, K. (2022). Robots used in therapy for children with autism spectrum disorder. American Journal of Multidisciplinary Research & Development (AJMRD), 4(03):33–39.

Guilherme Luz Tortorella, Flavio S. Fogliatto, Kleber Francisco Espôsto, Alejandro Mac Cawley Vergara, Roberto Vassolo, Diego Tlapa Mendoza, and Gopalakrishnan Narayanamurthy. Measuring the effect of healthcare 4.0 implementation on hospitals’ performance. Production Planning & Control, 33(4):386–401, 2022.

Gulli, A. and Pal, S. (2017). Deep learning with Keras. Packt Publishing Ltd.

Gunraj, H., Wang, L., and Wong, A. (2020). Covidnet-ct: A tailored deep convolutional neural network design for detection of covid-19 cases from chest ct images. Frontiers in Medicine, 7:1025.

Günther, C. W. and van der Aalst, W. M. P. (2007). Fuzzy mining – adaptive process simplification based on multi-perspective metrics. In Alonso, G., Dadam, P., and Rosemann, M., editors, Business Process Management, pages 328–343, Berlin, Heidelberg. Springer Berlin Heidelberg.

H. Diegoli, P. S. C. Magalhães, S. C. O. Martins, C. H. C. Moro, P. H. C. França, J. Safanelli, V. Nagel, V. G. Venancio, R. B. Liberato, and A. L. Longo. Decrease in hospital admissions for transient ischemic attack, mild, and moderate stroke during the covid-19 era. Stroke, 51(8):2315–2321, 2020.

H. I. Ozercan, A. M. Ileri, E. Ayday, and C. Alkan. Realizing the potential of blockchain technologies in genomics. Genome Res, 28(9):1255–1263, 2018.

Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., Kern, R., Picus, M., Hoyer, S., van Kerkwijk, M. H., Brett, M., Haldane, A., del Río, J. F., Wiebe, M., Peterson, P., Gérard-Marchant, P., Sheppard, K., Reddy, T.,Weckesser,W., Abbasi, H., Gohlke, C., and Oliphant, T. E. (2020). Array programming with NumPy. Nature, 585(7825):357–362.

Hillegonda Maria Dutilh Novaes and Patricia Coelho De Soárez. Health technologies assessment: origins, development, and current challenges. in the international and brazilian scenarios. Cadernos de Saude Publica, 36(8), 2020.

HIMSS. Healthcare Information and Management Systems Society, April 2021.

Hirschberg, D. S. (1975). A linear space algorithm for computing maximal common subsequences. Communications of the ACM, 18(6):341–343.

Hripcsak George, Albers David J, High-fidelity phenotyping: richness and freedom from bias, Journal of the American Medical Informatics Association, Volume 25, Issue 3, March (2018), Pages 289–294, https://doi.org/10.1093/jamia/ocx110

Hripcsak, G., N. Shang, P. L. Peissig, L. V. Rasmussen, C. Liu, B. Benoit, R. J. Carroll, et al. (2019). “Facilitating phenotype transfer using a common data model.” J Biomed Inform, July, 103253.

Huang, Z., Dong, W., Duan, H., and Li, H. (2014). Similarity measure between patient traces for clinical pathway analysis: Problem, method, and applications. IEEE Journal of Biomedical and Health Informatics, 18(1):4–14.

Huang, Z., Dong, W., Ji, L., Yin, L., and Duan, H. (2015). On local anomaly detection and analysis for clinical pathways. Artificial Intelligence in Medicine, 65(3):167–177.

Huang, Z., Dong,W., Ji, L., He, C., and Duan, H. (2016). Incorporating comorbidities into latent treatment pattern mining for clinical pathways. Journal of Biomedical Informatics, 59:227–239.

Huang, Z., Ge, Z., Dong, W., He, K., and Duan, H. (2018). Probabilistic modeling personalized treatment pathways using electronic health records. Journal of Biomedical Informatics, 86:33–48.

Huang, Z., Lu, X., and Duan, H. (2012). On mining clinical pathway patterns from medical behaviors. Artificial Intelligence in Medicine, 56(1):35–50.

Huang, Z., Lu, X., Duan, H., and Fan, W. (2013). Summarizing clinical pathways from event logs. Journal of Biomedical Informatics, 46(1):111–127.

Hunter, J. D. (2007). Matplotlib: A 2d graphics environment. Computing in Science & Engineering, 9(3):90–95.

Hur, C., Wi, J., and Kim, Y. (2020). Facilitating the development of deep learning models with visual analytics for electronic health records. International Journal of Environmental Research and Public Health, 17(22):1–14.

Huskens, B., Verschuur, R., Gillesen, J., Didden, R., and Barakova, E. (2013). Promoting question-asking in school-aged children with autism spectrum disorders: Effectiveness of a robot intervention compared to a human-trainer intervention. Developmental neurorehabilitation, 16(5):345–356.

I. Ghersi, M. Marino, and M. T. Miralles. Smart medical beds in patient-care environments of the twenty-first century: a state-of-art survey. BMC Med Inform Decis Mak, 18(1):63, 2018.

Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep Learning. MIT Press, Cambridge, 1 edition, 2017.

Iñigo Calvo-Sotomayor and Ekhi Atutxa. Reviewing the benefits of aging populations: Care activities provided by the older people as a commons. Frontiers in public health, 10:792287–792287, 2022.

Ito, M. (2020). Chapter 6 - patient-centered care. In Gogia, S., editor, Fundamentals of Telemedicine and Telehealth, pages 115–126. Academic Press.

J. E. Carolan, J. McGonigle, A. Dennis, P. Lorgelly, and A. Banerjee. Technologyenabled, evidence-driven, and patient-centered: The way forward for regulating software as a medical device. JMIR Med Inform, 10(1):e34038, 2022.

J. S. Scherer, J. S. Pereira, M. S. Debastiani, and C. G. Bica. Beyond technology: Can artificial intelligence support clinical decisions in the prediction of sepsis? Rev Bras Enferm, 75(5):e20210586, 2022.

J. Zarrin, H.Wen Phang, L. Babu Saheer, and B. Zarrin. Blockchain for decentralization of internet: prospects, trends, and challenges. Cluster Comput, pages 1–26, 2021.

Jacobi, A., Chung, M., Bernheim, A., and Eber, C. (2020). Portable chest X-ray in coronavirus disease-19 (COVID-19): A pictorial review. Clinical Imaging, 64:35–42.

Jagatheeswari, P., Kumar, S. S., and Rajaram, M. (2009). Contrast stretching recursively separated histogram equalization for brightness preservation and contrast enhancement. In 2009 International Conference on Advances in Computing, Control, and Telecommunication Technologies, pages 111–115.

Janssenswillen, G., Depaire, B., Swennen, M., Jans, M., and Vanhoof, K. (2019). bupar: Enabling reproducible business process analysis. Knowledge-Based Systems, 163:927–930.

Jawahar Jagarapu and Rashmin C. Savani. A brief history of telemedicine and the evolution of teleneonatology. Seminars in Perinatology, 45(5):151416, 2021.

Jesper E Van Engelen and Holger H Hoos. A survey on semi-supervised learning. Machine Learning, 109(2):373–440, 2020.

Joao Luis Zeni Montenegro, Cristiano André da Costa, and Rodrigo da Rosa Righi. Survey of conversational agents in health. Expert Systems with Applications, 129:56–67, 2019.

Johannsen, W. (1909). Elemente der exakten Erblichkeitslehre [Elements of the exact theory of heredity] (in German). Jena, Germany: Gustav Fischer Lane, Jennifer C E, James Weaver, Kristin Kostka, Talita Duarte-Salles, Maria Tereza F Abrahao, Heba Alghoul, Osaid Alser, et al. (2020). “Risk of Hydroxychloroquine Alone and in Combination with Azithromycin in the Treatment of Rheumatoid Arthritis: A Multinational, Retrospective Study.” The Lancet Rheumatology 2 (11): e698–711. https://doi.org/10.1016/S2665-9913(20)30276-9.

Jonathan Bach. A quick reference on hypoxemia. Veterinary Clinics: Small Animal Practice, 47(2):175–179, 2017.

Josué, M., Montevecchi, E., Abreu, R., Barreto, F., Santos, J., and Muchaluat-Saade, D. C. (2020). Ambientes multissensoriais aplicados à saúde: desenvolvimento de aplicações e tendências futuras. In Livro de Minicursos do SBCAS 2020, chapter 2. SBC.

Jun, M., Cheng, G., Yixin,W., Xingle, A., Jiantao, G., Ziqi, Y., Minqing, Z., Xin, L., Xueyuan, D., Shucheng, C., Hao,W., Sen, M., Xiaoyu, Y., Ziwei, N., Chen, L., Lu, T., Yuntao, Z., Qiongjie, Z., Guoqiang, D., and Jian, H. (2020). COVID-19 CT Lung and Infection Segmentation Dataset. Zenodo.

Júnior, E. A. and Yamashita, H. (Maio 2001). Aspectos básicos de tomografia computadorizada e ressonância magnética. Brazilian Journal of Psychiatry, 23.

K. Tsoi, K. Yiu, H. Lee, H. M. Cheng, T. D.Wang, J. C. Tay, B.W. Teo, Y. Turana, A. A. Soenarta, G. P. Sogunuru, S. Siddique, Y. C. Chia, J. Shin, C. H. Chen, J. G. Wang, and K. Kario. Applications of artificial intelligence for hypertension management. J Clin Hypertens (Greenwich), 23(3):568–574, 2021.

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778, 2016.

Kalb, K.A. and Conner-Von, S.O. (2019) “Holistic Nursing Education: Teaching in a holistic way”, Nurs. Educ. Perspect, https://doi.org/10.1097/01.NEP.0000000000000405.

Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014.

Kaufman, L. and Rousseeuw, P. (1990). Partitioning Around Medoids (Program PAM), chapter 2, pages 68–125. John Wiley & Sons, Ltd.

Kazimierczuk, M. and Jozwik, J. (1990). Analysis and design of class e zero-current-switching rectifier. IEEE Transactions on Circuits and Systems, 37(8):1000–1009.

Kempa-Liehr, A.W., Lin, C. Y. C., Britten, R., Armstrong, D., Wallace, J., Mordaunt, D., and O’Sullivan, M. (2020). Healthcare pathway discovery and probabilistic machine learning. International Journal of Medical Informatics, 137.

Khaled El Emam, Sam Rodgers, and Bradley Malin. Anonymising and sharing individual patient data. BMJ (Clinical research ed.), 350:h1139–h1139, 2015.

Khan, A., Uddin, S., and Srinivasan, U. (2018). Comorbidity network for chronic disease: A novel approach to understand type 2 diabetes progression. International Journal of Medical Informatics, 115:1–9.

Kim, E., Kim, S., Song, M., Kim, S., Yoo, D., Hwang, H., and Yoo, S. (2013). Discovery of outpatient care process of a tertiary university hospital using process mining. Healthcare Informatics Research, 19(1):42–49.

Kinsman, L., Rotter, T., James, E., Snow, P., and Willis, J. (2010). What is a clinical pathway? development of a definition to inform the debate. BMC medicine, 8.

Kurniati, A. P., Rojas, E., Hogg, D., Hall, G., and Johnson, O. A. (2019). The assessment of data quality issues for process mining in healthcare using Medical Information Mart for Intensive Care III, a freely available e-health record database. Health Informatics Journal, 25(4):1878–1893.

L. R. Waitman, I. E. Phillips, A. B. McCoy, I. Danciu, R. M. Halpenny, C. L. Nelsen, D. C. Johnson, J. M. Starmer, and J. F. Peterson. Adopting real-time surveillance dashboards as a component of an enterprisewide medication safety strategy. Jt Comm J Qual Patient Saf, 37(7):326–32, 2011.

Le Meur, N., Gao, F., and Bayat, S. (2015). Mining care trajectories using health administrative information systems: The use of state sequence analysis to assess disparities in prenatal care consumption. BMC Health Services Research, 15(1).

LeCun, Y., Bengio, Y., and Hinton, G. (2015). Deep learning. Nature, 521(7553):436–444.

Lecun, Y., Bottou, L., Bengio, Y., and Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278–2324.

Leemans, S. J., Fahland, D., and van der Aalst, W. M. (2013). Discovering block-structured process models from event logs-a constructive approach. In International conference on applications and theory of Petri nets and concurrency, pages 311–329. Springer.

Leonardi, G., Striani, M., Quaglini, S., Cavallini, A., and Montani, S. (2018). Leveraging semantic labels for multi-level abstraction in medical process mining and trace comparison. Journal of Biomedical Informatics, 83:10–24.

Levenshtein, V. I. et al. (1966). Binary codes capable of correcting deletions, insertions, and reversals. In Soviet physics doklady, volume 10, pages 707–710. Soviet Union.

Li, J. and Chignell, M. (2011). Communication of emotion in social robots through simple head and arm movements. International Journal of Social Robotics, 3(2):125–142.

Li, Xintong, Anna Ostropolets, Rupa Makadia, Azza Shoaibi, Gowtham Rao, Anthony G. Sena, Eugenia Martinez-Hernandez, et al. (2021). “Characterising the Background Incidence Rates of Adverse Events of Special Interest for Covid-19 Vaccines in Eight Countries: Multinational Network Cohort Study.” BMJ 373 (June): n1435. https://doi.org/10.1136/bmj.n1435.

Li, Y., Gu, C., Dullien, T., Vinyals, O., and Kohli, P. (2019). Graph matching networks for learning the similarity of graph structured objects. In International conference on machine learning, pages 3835–3845. PMLR.

Lin, F.-r., Chou, S.-c., Pan, S.-m., and Chen, Y.-m. (2001). Mining time dependency patterns in clinical pathways. International Journal of Medical Informatics, 62(1):11–25.

Links, J.M. et al. (2018) “Copewell: A Conceptual Framework and System Dynamics Model for Predicting Community Functioning and Resilience After Disasters”, Disaster Med Public Health Prep, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8743042/.

Lira, R., Salas-Morales, J., Leiva, L., de la Fuente, R., Fuentes, R., Delfino, A., Nazal, C. H., Sepúlveda, M., Arias, M., Herskovic, V., and Munoz-Gama, J. (2019). Process-Oriented Feedback through Process Mining for Surgical Procedures in Medical Training: The Ultrasound-Guided Central Venous Catheter Placement Case. International Journal of Environmental Research and Public Health, 16(11):1877.

Lismont, J., Janssens, A. S., Odnoletkova, I., vanden Broucke, S., Caron, F., and Vanthienen, J. (2016). A guide for the application of analytics on healthcare processes: A dynamic view on patient pathways. Computers in Biology and Medicine, 77:125–134.

Liu, C., Ishi, C. T., Ishiguro, H., and Hagita, N. (2012). Generation of nodding, head tilting and eye gazing for human-robot dialogue interaction. In 2012 7th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pages 285–292. IEEE.

Louis Engelbrecht, Adele Botha, and Ronell Alberts. Designing the visualization of information. 15(02):1540005, 2015.

Lu, F., Zeng, Q., and Duan, H. (2016). Synchronization-core-based discovery of processes with decomposable cyclic dependencies. ACM Transactions on Knowledge Discovery from Data, 10(3).

Lytridis, C., Vrochidou, E., Chatzistamatis, S., and Kaburlasos, V. (2018). Social engagement interaction games between children with autism and humanoid robot nao. In The 13th international conference on soft computing models in industrial and environmental applications, pages 562–570. Springer.

M. A. Dziadzko, V. Herasevich, A. Sen, B.W. Pickering, A. M. Knight, and P. Moreno Franco. User perception and experience of the introduction of a novel critical care patient viewer in the icu setting. Int J Med Inform, 88:86–91, 2016.

M. E. Kruk, A. D. Gage, C. Arsenault, K. Jordan, H. H. Leslie, S. Roder-DeWan, O. Adeyi, P. Barker, B. Daelmans, S. V. Doubova, M. English, E. Garcia-Elorrio, F. Guanais, O. Gureje, L. R. Hirschhorn, L. Jiang, E. Kelley, E. T. Lemango, J. Liljestrand, A. Malata, T. Marchant, M. P. Matsoso, J. G. Meara, M. Mohanan, Y. Ndiaye, O. F. Norheim, K. S. Reddy, A. K. Rowe, J. A. Salomon, G. Thapa, N. A. Y. Twum-Danso, and M. Pate. High-quality health systems in the sustainable development goals era: time for a revolution. Lancet Glob Health, 6(11):e1196–e1252, 2018.

M. I. Schmidt, B. B. Duncan, G. Azevedo e Silva, A. M. Menezes, C. A. Monteiro, S. M. Barreto, D. Chor, and P. R. Menezes. Chronic non-communicable diseases in brazil: burden and current challenges. Lancet, 377(9781):1949–61, 2011.

M. J. Prince, F. Wu, Y. Guo, L. M. Gutierrez Robledo, M. O’Donnell, R. Sullivan, and S. Yusuf. The burden of disease in older people and implications for health policy and practice. Lancet, 385(9967):549–62, 2015.

Ma, J., Wang, Y., An, X., Ge, C., Yu, Z., Chen, J., Zhu, Q., Dong, G., He, J., He, Z., Cao, T., Zhu, Y., Nie, Z., and Yang, X. (2021). Toward data-efficient learning: A benchmark for covid-19 ct lung and infection segmentation. Medical Physics, 48(3):1197–1210.

Macq Lopes, G. M. M. Oliveira, and L. M. Maia. Digital health, universal right, duty of the state? Arq Bras Cardiol, 113(3):429–434, 2019.

MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. In Proc. 5th Berkeley Symp. Math. Stat. Probab., Univ. Calif. 1965/66, 1, 281-297 (1967).

Madigan, D., P. B. Ryan, M. Schuemie, P. E. Stang, J. M. Overhage, A. G. Hartzema, M. A. Suchard, W. DuMouchel, and J. A. Berlin. (2013). “Evaluating the impact of database heterogeneity on observational study results.” Am. J. Epidemiol. 178 (4): 645–51.

Mahsa Shabani. Blockchain-based platforms for genomic data sharing: a decentralized approach in response to the governance problems? Journal of the American Medical Informatics Association, 26(1):76–80, 2018.

Man Zhang, Yong Zhou, Jiaqi Zhao, Yiyun Man, Bing Liu, and Rui Yao. A survey of semi-and weakly supervised semantic segmentation of images. Artificial Intelligence Review, 53(6):4259–4288, 2020.

Manktelow, M., Iftikhar, A., Bucholc, M., McCann, M., and O’Kane, M. (2022). Clinical and operational insights from data-driven care pathway mapping: a systematic review. BMC Medical Informatics and Decision Making, 22(1):43.

Manohar, V., al Marzooqi, S., and Crandall, J. W. (2011). Expressing emotions through robots: a case study using off-the-shelf programming interfaces. In 2011 6th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pages 199–200, Japão. IEEE.

Marazza, F., Bukhsh, F. A., Geerdink, J., Vijlbrief, O., Pathak, S., van Keulen, M., and Seifert, C. (2020). Automatic process comparison for subpopulations: Application in cancer care. International Journal of Environmental Research and Public Health, 17(16):1–23.

Marjan Ghazisaeidi, Reza Safdari, Mashallah Torabi, Mahboobeh Mirzaee, Jebraeil Farzi, and Azadeh Goodini. Development of performance dashboards in healthcare sector: Key practical issues. Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH, 23(5):317–321, 2015.

Marques, J. V., Veras, R., and Silva, R. R. (2022). Detection of covid-19 in computed tomography images using deep learning: A literature review. Revista de Sistemas e Computação-RSC, 12(1).

Marques, J., Veras, R., and Silva, R. (2021). A literature review: Detection of covid-19 in computed tomography images using deep learning. In Anais do XIV Encontro Unificado de Computação do Piauí e XI Simpósio de Sistemas de Informação, pages 9–16, Porto Alegre, RS, Brasil. SBC.

Marsella, S., Xu, Y., Lhommet, M., Feng, A., Scherer, S., and Shapiro, A. (2013). Virtual character performance from speech. In Proceedings of the 12th ACM SIGGRAPH/Eurographics Symposium on Computer Animation, pages 25–35.

Martin, K.S. (2005) “The Omaha System: A Key to Practice, Documentation, and Information Management” (Reprinted 2nd ed.). Omaha, NE: Health Connections Press.

Martínez Chamorro, E., Díez Tascón, A., Ibáñez Sanz, L., Ossaba Vélez, S., and Borruel Nacenta, S. (2021). Diagnóstico radiológico del paciente con covid-19. Radiología, 63(1):56–73.

Martinez-Martin, E., Escalona, F., and Cazorla, M. (2020). Socially assistive robots for older adults and people with autism: An overview. Electronics, 9(2):367.

Matarić, M. J., Eriksson, J., Feil-Seifer, D. J., and Winstein, C. J. (2007). Socially assistive robotics for post-stroke rehabilitation. Journal of neuroengineering and rehabilitation, 4(1):1–9.

Mazzei, D., Lazzeri, N., Billeci, L., Igliozzi, R., Mancini, A., Ahluwalia, A., Muratori, F., and De Rossi, D. (2011). Development and evaluation of a social robot platform for therapy in autism. In 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pages 4515–4518. IEEE.

McDonald, K. M., Sundaram, V., Bravata, D. M., Lewis, R., Lin, N., Kraft, S. A., McKinnon, M., Paguntalan, H., and Owens, D. K. (2007). Closing the quality gap: a critical analysis of quality improvement strategies (vol. 7: Care coordination). Technical Report 04(07)-0051-7, Agency for Healthcare Research and Quality. Technical Review 9 (Preparedby the Stanford University-UCSF Evidencebased Practice Center under contract 290-02-0017).

Mertens, S., Gailly, F., and Poels, G. (2018). Discovering healthcare processes using DeciClareMiner. Health Systems, 7(3):195–211.

Mertens, S., Gailly, F., Van Sassenbroeck, D., and Poels, G. (2020). Integrated Declarative Process and Decision Discovery of the Emergency Care Process. Information Systems Frontiers.

Mesibov, G. B., Adams, L. W., and Klinger, L. G. (2013). Autism: Understanding the disorder. Springer Science & Business Media.

Mingxing Tan and Quoc Le. Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning, pages 6105–6114. PMLR, 2019.

Ministério da Saúde Brasil, Secretaria de Vigilância em Saúde, and Departamento de Análise em Saúde e Vigilância de Doenças Não Transmissíveis. Plano de ações estratégicas para o enfrentamento das doenças crônicas e agravos não transmissíveis no brasil 2021-2030. Ministério da Saúde, 118, 2021.

Ministério da Saúde Brasil, Secretaria de Vigilância em Saúde, and Departamento de Análise em Saúde e Vigilância de Doenças Não Transmissíveis. Vigitel brasil 2019 : vigilância de fatores de risco e proteção para doenças crônicas por inquérito telefônico: estimativas sobre frequência e distribuição sociodemográfica de fatores de risco e proteção para doenças crônicas nas capitais dos 26 estados brasileiros e no distrito federal em 2019. Ministério da Saúde, 137, 2021.

Ministério da Saúde Brasil. Contas de saúde na perspectiva da contabilidade internacional: conta sha para o brasil, 2015 a 2019. 2022.

Mitjans, A. A. (2020). Affective computation in human-robot interaction. Master thesis, Centro de Investigación Científica y de Educación Superior de Ensenada, Baja California.

Monsen, K.A. (2018) “The Omaha system as an ontology and meta-model for nursing and healthcare in an era of Big Data”, Kontakt, http://dx.doi.org/10.1016/j.kontakt.2018.03.001.

Monsen, K.A. et al. (2021a) “Incorporating a Whole-Person Perspective in Consumer-Generated Data: Social Determinants, Resilience, and Hidden Patterns”, Comput. Inform. Nurs, [link].

Monsen, K.A. et al. (2021b) “Exploring Large Community- and Clinically-Generated Datasets to Understand Resilience Before and During the COVID-19 Pandemic”, J. Nurs. Scholarship, https://doi.org/10.1111/jnu.12634.

Mordoch, E., Osterreicher, A., Guse, L., Roger, K., and Thompson, G. (2013). Use of social commitment robots in the care of elderly people with dementia: A literature review. Maturitas, 74(1):14–20.

Moreira, F. A., De Almeida, L. A., and Galv˜ao, A. (2017). Guia de Diagnóstico por Imagem: O passo a passo que todo médico deve saber. Elsevier Brasil.

Moreiran, F. (2017). Guia de diagnóstico por imagem: O passo a passo que todo médico deve saber. GEN Guanabara Koogan.

MSMH - MyStrengths MyHealth™ (2022), https://license.umn.edu/product/mystrengths-myhealth.

Murugan Subramanian, Anne Wojtusciszyn, Lucie Favre, Sabri Boughorbel, Jingxuan Shan, Khaled B. Letaief, Nelly Pitteloud, and Lotfi Chouchane. Precision medicine in the era of artificial intelligence: implications in chronic disease management. Journal of translational medicine, 18(1):472–472, 2020.

N. Rifi, E. Rachkidi, N. Agoulmine, and N. C. Taher. Towards using blockchain technology for ehealth data access management. In 2017 Fourth International Conference on Advances in Biomedical Engineering (ICABME), pages 1–4.

Najjar, A., Reinharz, D., Girouard, C., and Gagné, C. (2018). A two-step approach for mining patient treatment pathways in administrative healthcare databases. Artificial Intelligence in Medicine, 87:34–48.

Nestorov, N., Stone, E., Lehane, P., and Eibrand, R. (2014). Aspects of socially assistive robots design for dementia care. In 2014 IEEE 27th International Symposium on Computer-Based Medical Systems, pages 396–400. IEEE.

NHS England and NHS Improvement (2022). Flow – reduce unnecessary waits. Disponível em [link]. Acesso em 28-05-2022.

Norberto Luiz Cabral, Vivian Nagel, Adriana B. Conforto, Pedro S. C. Magalhaes, Vanessa G. Venancio, Juliana Safanelli, Felipe Ibiapina, Suleimy Mazin, Paulo França, Rafaela M. Liberato, Alexandre Longo, and Viviane F. Zetola. High fiveyear mortality rates of ischemic stroke subtypes: A prospective cohort study in brazil. International Journal of Stroke, 14(5):491–499, 2018.

Novák, M. (2010). Easy implementation of domain specific language using xml. In Proceedings of the 10th Scientific Conference of Young Researchers (SCYR 2010), Košice, Slovakia, volume 19.

OECD. OECD Reviews of Health Systems: Brazil 2021. 2021.

OHDSI https://www.ohdsi.org/

OHDSI Observational Health Data Sciences and Informatics. (2021 a). HADES. Health Analytics Data-toEvidence Suite (HADES): A collection of R packages for performing analytics against the Common Data Model. Retrieved June 15, 2021, from https://ohdsi.github.io/Hades/

OHDSI Observational Health Data Sciences and Informatics. (2021). Cohort Diagnostics. Cohort Diagnostics: An R package for performing various cohort diagnostics. Retrieved June 15, 2021, from https://ohdsi.github.io/CohortDiagnostics/

OHDSI. The book of OHDSI https://github.com/OHDSI/TheBookOfOhdsi Este livro está licenciado sob Creative Commons Zero v1.0 Universal.

Oktay, O., Schlemper, J., Folgoc, L. L., Lee, M. C. H., Heinrich, M. P., Misawa, K., Mori, K., McDonagh, S. G., Hammerla, N. Y., Kainz, B., Glocker, B., and Rueckert, D. (2018). Attention u-net: Learning where to look for the pancreas. CoRR, abs/1804.03999.

Olsen LA, Aisner D, McGinnis JM, editors. Institute of Medicine (US) Roundtable on Evidence-Based Medicine; The Learning Healthcare System: Workshop Summary. Washington (DC): National Academies Press (US); (2007). Institute of Medicine Roundtable on Evidence-Based Medicine. Available from: https://www.ncbi.nlm.nih.gov/books/NBK53483/

Onur Asan, Alparslan Emrah Bayrak, and Avishek Choudhury. Artificial intelligence and human trust in healthcare: Focus on clinicians. Journal of medical Internet research, 22(6):e15154–e15154, 2020.

Organization, W. H. (2022). Weekly epidemiological update on covid-19 - 1 march 2022. Acessado: 18-05-2022.

Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1):62–66.

P. Caroline Gonçales, D. Pinto Júnior, P. de Oliveira Salgado, and T. C. Machado Chianca. Relationship between risk stratification, mortality and length of stay in a emergency hospital. Invest Educ Enferm, 33(3):424–431, 2015.

Panos Alexopoulos. Semantic Modeling for Data. O’Reilly Media, 1st ed. edition, 2020.

Partington, A., Wynn, M., Suriadi, S., Ouyang, C., and Karnon, J. (2015). Process mining for clinical processes: A comparative analysis of four australian hospitals. ACM Transactions on Management Information Systems, 5(4).

Pastorino, R., De Vito, C., Migliara, G., Glocker, K., Binenbaum, I., Ricciardi, W., and Boccia, S. (2019). Benefits and challenges of big data in healthcare: an overview of the european initiatives. European journal of public health, 29(Supplement_3):23–27.

Paula A. Bracco, Edward W. Gregg, Deborah B. Rolka, Maria Inês Schmidt, Sandhi M. Barreto, Paulo A. Lotufo, Isabela Bensenor, and Bruce B. Duncan. Lifetime risk of developing diabetes and years of life lost among those with diabetes in brazil. Journal of global health, 11:04041–04041, 2021.

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al. (2011). Scikit-learn: Machine learning in python. Journal of machine learning research, 12(Oct):2825–2830.

Pelachaud, C., Badler, N. I., and Steedman, M. (1996). Generating facial expressions for speech. Cognitive science, 20(1):1–46.

Perer, A., Wang, F., and Hu, J. (2015). Mining and exploring care pathways from electronic medical records with visual analytics. Journal of Biomedical Informatics, 56:369–378.

Peter Wegner. Interoperability. ACM Computing Surveys (CSUR), 28(1):285–287, 1996.

Pianykh, O. S. and Rosenthal, D. I. (2015). Can we predict patient wait time? Journal of the American College of Radiology, 12(10):1058– 1066.

Poitras, M.-E., Maltais, M.-E., Bestard-Denommé, L., Stewart, M., and Fortin, M. (2018). What are the effective elements in patient-centered and multimorbidity care? A scoping review. BMC health services research, 18(1):1–9.

Pollmann, K., Tagalidou, N., and Fronemann, N. (2019). It’s in your eyes: Which facial design is best suited to let a robot express emotions? In Proceedings of Mensch und Computer 2019, pages 639–642.

Ponti, M. A. and da Costa, G. B. P. (Tópicos em Gerenciamento de Dados e Informac˜oes, 2017). Como funciona o deep learning. SBC, 1a ed., 23.

Pritika Parmar, Jina Ryu, Shivani Pandya, João Sedoc, and Smisha Agarwal. Health-focused conversational agents in person-centered care: a review of apps. NPJ digital medicine, 5(1):21–21, 2022.

Prodel, M., Augusto, V., Jouaneton, B., Lamarsalle, L., and Xie, X. (2018). Optimal Process Mining for Large and Complex Event Logs. IEEE Transactions on Automation Science and Engineering, 15(3):1309–1325.

Prokofyeva, E. S. and Zaytsev, R. D. (2020). Clinical pathways analysis of patients in medical institutions based on hard and fuzzy clustering methods. Business Informatics, 14(1):19–31.

R. Rocha, I. Furtado, and P. Spinola. Financing needs, spending projection, and the future of health in brazil. Health Econ, 30(5):1082–1094, 2021.

Rcpd Reis, B. B. Duncan, D. C. Malta, B. P. M. Iser, and M. I. Schmidt. Evolution of diabetes in brazil: prevalence data from the 2013 and 2019 brazilian national health survey. Cad Saude Publica, 38Suppl 1(Suppl 1):e00149321, 2022.

Rebuge, Á. and Ferreira, D. R. (2012). Business process analysis in healthcare environments: A methodology based on process mining. Information Systems, 37(2):99–116.

Richard S Hotchkiss, Lyle L Moldawer, Steven M Opal, Konrad Reinhart, Isaiah R Turnbull, and Jean-Louis Vincent. Sepsis and septic shock. Nature reviews Disease primers, 2(1):1–21, 2016.

Richard S Sutton and Andrew G Barto. Reinforcement learning: An introduction. MIT press, 2018.

Ricks, D. J. and Colton, M. B. (2010). Trends and considerations in robot-assisted autism therapy. In 2010 IEEE international conference on robotics and automation, pages 4354–4359. IEEE.

Ricks, D. J., Colton, M. B., and Goodrich, M. A. (2010). Design and evaluation of a clinical upper-body humanoid robot for autism therapy. In In Proceedings of the 2010 International Conference on Applied Bionics and Biomechanics, Venice, Italy, pages 14–16.

Riegel, F. et al. (2021) “Florence Nightingale’s theory and her contributions to holistic critical thinking in nursing”, Revista Brasileira de Enfermagem, https://doi.org/10.1590/0034-7167-2020-0139.

Rinner, C., Helm, E., Dunkl, R., Kittler, H., and Rinderle-Ma, S. (2018). Process mining and conformance checking of long running processes in the context of melanoma surveillance. International Journal of Environmental Research and Public Health, 15(12).

Rismanchian, F. and Lee, Y. H. (2017). Process Mining– Based Method of Designing and Optimizing the Layouts of Emergency Departments in Hospitals. Health Environments Research and Design Journal, 10(4):105– 120.

Robinson, H., MacDonald, B., and Broadbent, E. (2014). The role of healthcare robots for older people at home: A review. International Journal of Social Robotics, 6(4):575–591.

Rocha, M., Valentim, P., Barreto, F., Mitjans, A., Cruz-Sandoval, D., Favela, J., and C., M.-S. D. (2021). Towards enhancing the multimodal interaction of a social robot to assist children with autism in emotion regulation. In Proceedings of the 15th EAI International Conference on Pervasive Computing Technologies for Healthcare.

Rodrigo Pereira Duquia, João Luiz Bastos, Renan Rangel Bonamigo, David Alejandro González-Chica, and Jeovany Martínez-Mesa. Presenting data in tables and charts. Anais brasileiros de dermatologia, 89(2):280–285, 2014.

Rogers, H., Maher, L., and Plsek, P. E. (2008). New rules for driving innovation in access to secondary care in the NHS.

Ronneberger, O., Fischer, P., and Brox, T. (2015a). U-net: Convolutional networks for biomedical image segmentation. In Navab, N., Hornegger, J., Wells, W. M., and Frangi, A. F., editors, Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, pages 234–241, Cham. Springer International Publishing.

Ronneberger, O., P.Fischer, and Brox, T. (2015b). U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention (MICCAI), volume 9351 of LNCS, pages 234–241. Springer. (available on arXiv:1505.04597 [cs.CV]).

Ronneberger, O., P.Fischer, and Brox, T. (2015c). U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention (MICCAI), volume 9351 of LNCS, pages 234–241. Springer. (available on arXiv:1505.04597 [cs.CV]).

Rosa, M. E. E., Matos, M. J. R. d., Furtado, R. S. O. d. P., Brito, V. M., Amaral, L. T. W., Beraldo, G. L., Fonseca, E. K. U. N., Chate, R. C., Passos, R. B. D., Teles, G. B. d. S., Silva, M. M. A., Yokoo, P., Yanata, E., Shoji, H., Szarf, G., and Funari, M. B. d. G. (2020). COVID-19 findings identified in chest computed tomography: a pictorial essay. Einstein (Sao Paulo), 18:eRW5741.

Rotondi, A. J., Brindis, C., Cantees, K. K., Deriso, B. M., Ilkin, H. M., Palmer, J. S., Gunnerson, H. B., and Watkins, W. D. (1997). Benchmarking the perioperative process. I. Patient routing systems: A method for continual improvement of patient flow and resource utilization. Journal of Clinical Anesthesia, 9(2):159–169.

Rubin, G. D., Ryerson, C. J., Haramati, L. B., Sverzellati, N., Kanne, J. P., Raoof, S., Schluger, N. W., Volpi, A., Yim, J.-J., Martin, I. B. K., Anderson, D. J., Kong, C., Altes, T., Bush, A., Desai, S. R., Goldin, o., Goo, J. M., Humbert, M., Inoue, Y., Kauczor, H.-U., Luo, F., Mazzone, P. J., Prokop, M., Remy-Jardin, M., Richeldi, L., Schaefer-Prokop, C. M., Tomiyama, N., Wells, A. U., and Leung, A. N. (2020). The role of chest imaging in patient management during the covid-19 pandemic: A multinational consensus statement from the fleischner society. Radiology, 296(1):172–180. PMID: 32255413.

S. Cruz Rivera, X. Liu, A. W. Chan, A. K. Denniston, and M. J. Calvert. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the spirit-ai extension. Nat Med, 26(9):1351–1363, 2020.

S. Gerke, B. Babic, T. Evgeniou, and I. G. Cohen. The need for a system view to regulate artificial intelligence/machine learning-based software as medical device. NPJ Digit Med, 3:53, 2020.

S. Nurk, S. Koren, A. Rhie, M. Rautiainen, A. V. Bzikadze, A. Mikheenko, M. R. Vollger, N. Altemose, L. Uralsky, A. Gershman, S. Aganezov, S. J. Hoyt, M. Diekhans, G. A. Logsdon, M. Alonge, S. E. Antonarakis, M. Borchers, G. G. Bouffard, S. Y. Brooks, G. V. Caldas, N. C. Chen, H. Cheng, C. S. Chin, W. Chow, L. G. de Lima, P. C. Dishuck, R. Durbin, T. Dvorkina, I. T. Fiddes, G. Formenti, R. S. Fulton, A. Fungtammasan, E. Garrison, P. G. S. Grady, T. A. Graves-Lindsay, I. M. Hall, N. F. Hansen, G. A. Hartley, M. Haukness, K. Howe, M. W. Hunkapiller, C. Jain, M. Jain, E. D. Jarvis, P. Kerpedjiev, M. Kirsche, M. Kolmogorov, J. Korlach, M. Kremitzki, H. Li, V. V. Maduro, T. Marschall, A. M. McCartney, J. McDaniel, D. E. Miller, J. C. Mullikin, E. W. Myers, N. D. Olson, B. Paten, P. Peluso, P. A. Pevzner, D. Porubsky, T. Potapova, E. I. Rogaev, J. A. Rosenfeld, S. L. Salzberg, V. A. Schneider, F. J. Sedlazeck, K. Shafin, C. J. Shew, A. Shumate, Y. Sims, A. F. A. Smit, D. C. Soto, I. Sović, J. M. Storer, A. Streets, B. A. Sullivan, F. Thibaud-Nissen, J. Torrance, J. Wagner, B. P. Walenz, A. Wenger, J. M. D. Wood, C. Xiao, S. M. Yan, A. C. Young, S. Zarate, U. Surti, R. C. McCoy, M. Y. Dennis, I. A. Alexandrov, J. L. Gerton, R. J. O’Neill, W. Timp, J. M. Zook, M. C. Schatz, E. E. Eichler, K. H. Miga, and A. M. Phillippy. The complete sequence of a human genome. Science, 376(6588):44–53, 2022.

S. Rouhani and S. Zamenian. An architectural framework for healthcare dashboards design. J Healthc Eng, 2021:1964054, 2021.

S. Shi, D. He, L. Li, N. Kumar, M. K. Khan, and K. R. Choo. Applications of blockchain in ensuring the security and privacy of electronic health record systems: A survey. Comput Secur, 97:101966, 2020.

Sadiya S. Khan, Benjamin D. Singer, and Douglas E. Vaughan. Molecular and physiological manifestations and measurement of aging in humans. Aging cell, 16(4):624–633, 2017.

Saldien, J., Goris, K., Vanderborght, B., Vanderfaeillie, J., and Lefeber, D. (2010). Expressing emotions with the social robot probo. International Journal of Social Robotics, 2(4):377–389.

Salichs, M. A., Encinar, I. P., Salichs, E., Castro-González, Á., and Malfaz, M. (2016). Study of scenarios and technical requirements of a social assistive robot for alzheimer’s disease patients and their caregivers. International Journal of Social Robotics, 8(1):85–102.

Samir, A., Naguib, N. N. N., Elnekeidy, A., Baess, A. I., and Shawky, A. (2021). Covid-19 versus h1n1: challenges in radiological diagnosis—comparative study on 130 patients using chest hrct. Egyptian Journal of Radiology and Nuclear Medicine, 52(1):77.

Sanfeliu, A. and Fu, K.-S. (1983). A distance measure between attributed relational graphs for pattern recognition. IEEE Transactions on Systems, Man, and Cybernetics, SMC-13(3):353–362.

Santatiwongchai, S., Kaewkamnerdpong, B., Jutharee, W., and Ounjai, K. (2016). Bliss: Using robot in learning intervention to promote social skills for autism therapy. In Proceedings of the International Convention on Rehabilitation Engineering & Assistive Technology, i-CREATe 2016, Midview City, SGP. Singapore Therapeutic, Assistive & Rehabilitative Technologies (START) Centre.

Sato, D. M., Mantovani, L. K., Safanelli, J., Guesser, V., Nagel, V., Moro, C. H., Cabral, N. L., Scalabrin, E. E., Moro, C., and Santos, E. A. (2020). Ischemic stroke: Process perspective, clinical and profile characteristics, and external factors. Journal of Biomedical Informatics, 111(1):103582.

Sawhney, S., Tan, Z., Black, C., Marks, A., Mclernon, D. J., Ronksley, P., and James, M. T. (2021). Validation of Risk Prediction Models to Inform Clinical Decisions After Acute Kidney Injury. American Journal of Kidney Diseases, 78(1):28–37.

Scassellati, B., Admoni, H., and Matarić, M. (2012). Robots for use in autism research. Annual review of biomedical engineering, 14:275–294.

Scot H. Simpson. Creating a data analysis plan: What to consider when choosing statistics for a study. The Canadian journal of hospital pharmacy, 68(4):311–317, 2015.

Senderovich, A., Weidlich, M., Yedidsion, L., Gal, A., Mandelbaum, A., Kadish, S., and Bunnell, C. A. (2016). Conformance checking and performance improvement in scheduled processes: A queueing-network perspective. Information Systems, 62:185–206.

Seneca Perri-Moore, Seraphine Kapsandoy, Katherine Doyon, Brent Hill, Melissa Archer, Laura Shane-McWhorter, Bruce E. Bray, and Qing Zeng-Treitler. Automated alerts and reminders targeting patients: A review of the literature. Patient education and counseling, 99(6):953–959, 2016.

Sequeira, K. and Zaki, M. (2002). Admit: Anomaly-based data mining for intrusions. In Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’02, page 386–395, New York, NY, USA. Association for Computing Machinery.

Shahid Ud Din Wani, Nisar Ahmad Khan, Gaurav Thakur, Surya Prakash Gautam, Mohammad Ali, Prawez Alam, Sultan Alshehri, Mohammed M. Ghoneim, and Faiyaz Shakeel. Utilization of artificial intelligence in disease prevention: Diagnosis, treatment, and implications for the healthcare workforce. Healthcare (Basel, Switzerland), 10(4):608, 2022.

Shamsuddin, S., Yussof, H., Ismail, L., Hanapiah, F. A., Mohamed, S., Piah, H. A., and Zahari, N. I. (2012). Initial response of autistic children in human-robot interaction therapy with humanoid robot nao. In 2012 IEEE 8th International Colloquium on Signal Processing and its Applications, pages 188–193. IEEE.

Shane Legg, Marcus Hutter, et al. A collection of definitions of intelligence. Frontiers in Artificial Intelligence and applications, 157:17, 2007.

Shibata, T. (2004). An overview of human interactive robots for psychological enrichment. Proceedings of the IEEE, 92(11):1749–1758.

Shibata, T. (2012). Therapeutic seal robot as biofeedback medical device: Qualitative and quantitative evaluations of robot therapy in dementia care. Proceedings of the IEEE, 100(8):2527–2538.

Simonyan, K. and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv 1409.1556.

Smdac Jayatilake and G. U. Ganegoda. Involvement of machine learning tools in healthcare decision making. J Healthc Eng, 2021:6679512, 2021.

Stefanini, A., Aloini, D., Benevento, E., Dulmin, R., and Mininno, V. (2018). Performance analysis in emergency departments: a data-driven approach. Measuring Business Excellence, 22(2):130–145.

Stefanini, A., Aloini, D., Benevento, E., Dulmin, R., and Mininno, V. (2020). A data-driven methodology for supporting resource planning of health services. Socio-Economic Planning Sciences, 70(October 2019):100744.

Stephanie Dick. Artificial intelligence. Harvard Data Science Review, 1(1), 2019.

Stephanie, S., Shum, T., Cleveland, H., Challa, S. R., Herring, A., Jacobson, F. L., Hatabu, H., Byrne, S. C., Shashi, K., Araki, T., Hernandez, J. A., White, C. S., Hossain, R., Hunsaker, A. R., and Hammer, M. M. (2020). Determinants of chest x-ray sensitivity for COVID-19: A multi-institutional study in the united states. Radiol Cardiothorac Imaging, 2(5):e200337.

Stuart Russell and Peter Norvig. Artificial intelligence: a modern approach. 2020.

Suchard, Marc A., Martijn J. Schuemie, Harlan M. Krumholz, Seng Chan You, RuiJun Chen, Nicole Pratt, Christian G. Reich, et al. (2019). “Comprehensive Comparative Effectiveness and Safety of First-Line Antihypertensive Drug Classes: A Systematic, Multinational, Large-Scale Analysis.” The Lancet 394 (10211): 1816–26. https://doi.org/10.1016/S0140-6736(19)32317-7.

Sun, Y., Teow, K. L., Heng, B. H., Ooi, C. K., and Tay, S. Y. (2012). Real-time prediction of waiting time in the emergency department, using quantile regression. Annals of Emergency Medicine, 60(3):299–308.

Sverzellati, N., Ryerson, C. J., Milanese, G., Renzoni, E. A., Volpi, A., Spagnolo, P., Bonella, F., Comelli, I., Affanni, P., Veronesi, L., Manna, C., Ciuni, A., Sartorio, C., Tringali, G., Silva, M., Michieletti, E., Colombi, D., andWells, A. U. (2021). Chest radiography or computed tomography for COVID-19 pneumonia? comparative study in a simulated triage setting. Eur. Respir. J., 58(3):2004188.

Swerdel, Joel N et al. “PheValuator: Development and evaluation of a phenotype algorithm evaluator.” Journal of biomedical informatics vol. 97 (2019): 103258. doi:10.1016/j.jbi.2019.103258

T. D. Ferguson and T. L. Howell. Bedside reporting: Protocols for improving patient care. Nurs Clin North Am, 50(4):735–47, 2015.

T. Hirano, T. Motohashi, K. Okumura, K. Takajo, T. Kuroki, D. Ichikawa, Y. Matsuoka, E. Ochi, and T. Ueno. Data validation and verification using blockchain in a clinical trial for breast cancer: Regulatory sandbox. J Med Internet Res, 22(6):e18938, 2020.

T. S. Amer and Sury Ravindran. The effect of visual illusions on the graphical display of information. Journal of Information Systems, 24(1):23–42, 2010.

T. Vilela de Sousa, Amrz Cavalcante, N. X. Lima, J. S. Souza, A. L. L. Sousa, V. V. Brasil, F. V. M. Vieira, J. V. Guimarães, M. A. de Matos, E. A. Silveira, and V. Pagotto. Cardiovascular risk factors in the elderly: a 10-year follow-up survival analysis. Eur J Cardiovasc Nurs, 2022.

Tamburis, O. and Esposito, C. (2020). Process mining as support to simulation modeling: A hospital-based case study. Simulation Modelling Practice and Theory, 104(June):102149.

Tang, A., Tam, R., Cadrin-Chênevert, A., Guest, W., Chong, J., Barfett, J., Chepelev, L., Cairns, R., Mitchell, J. R., Cicero, M. D., Poudrette, M. G., Jaremko, J. L., Reinhold, C., Gallix, B., Gray, B., Geis, R., and for the Canadian Association of Radiologists (CAR) Artificial Intelligence Working Group (2018). Canadian association of radiologists white paper on artificial intelligence in radiology. Canadian Association of Radiologists Journal, 69(2):120–135. PMID: 29655580.

Tapus, A., Mataric, M. J., and Scassellati, B. (2007). Socially assistive robotics [grand challenges of robotics]. IEEE robotics & automation magazine, 14(1):35–42.

Tapus, A., Tapus, C., and Mataric, M. J. (2009). The use of socially assistive robots in the design of intelligent cognitive therapies for people with dementia. In 2009 IEEE international conference on rehabilitation robotics, pages 924–929. IEEE.

Theodore H. Tulchinsky and Elena A. Varavikova. A history of public health. The New Public Health, pages 1–42, 2014.

Thiago Martini da Costa, Paulo Lísias Salomão, Amilton Souza Martha, Ivan Torres Pisa, and Daniel Sigulem. The impact of short message service text messages sent as appointment reminders to patients’ cell phones at outpatient clinics in são paulo, brazil. International Journal of Medical Informatics, 79(1):65–70, 2010.

Tim Benson and Grahame Grieve. Why interoperability is hard. In Principles of Health Interoperability, pages 21–40. Springer, 2021.

Timothy A. Salthouse. When does age-related cognitive decline begin? Neurobiology of aging, 30(4):507–514, 2009.

Tom M Mitchell et al. Machine learning. Burr Ridge, IL: McGraw Hill, 45(37):870–877, 1997.

Ueyama, Y. (2015). A bayesian model of the uncanny valley effect for explaining the effects of therapeutic robots in autism spectrum disorder. PloS one, 10(9):e0138642.

Unesco (2021), Draft recommendation on Open Science on its way to final adoption [link].

Ursoniu, S., Vernic, C., Muntean, C., and Timar, B. (2012). Nursing case management: Identifying, coordinating and monitoring the implementation of care services for patients. Annals. Computer Science Series, 10(2).

V. C. Schulz, P. S. C. de Magalhaes, C. C. Carneiro, J. I. T. da Silva, V. N. Silva, V. V. Guesser, J. Safanelli, H. Diegoli, R. B. Liberato, C. C. C. Lopes, A. de Souza, P. H. C. de França, A. B. Conforto, and N. L. Cabral. Improved outcomes after reperfusion therapies for ischemic stroke: A "real-world"study in a developing country. Curr Neurovasc Res, 17(4):361–375, 2020.

V. M. Pashkov, O. S. Soloviov, and Y. O. Harkusha. Challenges of classification of stand-alone software as a medical device. Wiad Lek, 74(2):327–333, 2021.

Vadim N. Gladyshev, Stephen B. Kritchevsky, Steven G. Clarke, Ana Maria Cuervo, Oliver Fiehn, João Pedro de Magalhães, Theresa Mau, Michal Maes, Robert L. Moritz, Laura J. Niedernhofer, Emile Van Schaftingen, Gregory J. Tranah, Kenneth Walsh, Yoshimitsu Yura, Bohan Zhang, and Steven R. Cummings. Molecular damage in aging. Nature Aging, 1(12):1096–1106, 2021.

Valentí Soler, M., Agüera-Ortiz, L., Olazarán Rodríguez, J., Mendoza Rebolledo, C., Pérez Muñoz, A., Rodríguez Pérez, I., Osa Ruiz, E., Barrios Sánchez, A., Herrero Cano, V., Carrasco Chillón, L., et al. (2015). Social robots in advanced dementia. Frontiers in aging neuroscience, 7:133.

Valentim, P. A., Barreto, F., and Muchaluat-Saade, D. C. (2020). Possibilitando o reconhecimento de expressões faciais em aplicações ginga-ncl. In Anais Estendidos do XXVI Simpósio Brasileiro de Sistemas Multimídia e Web, pages 53–56. SBC.

van der Aalst, W. M. (2019). A practitioner’s guide to process mining: Limitations of the directly-follows graph. Procedia Computer Science, 164:321–328.

Van der Walt, S., Schönberger, J. L., Nunez-Iglesias, J., Boulogne, F., Warner, J. D., Yager, N., Gouillart, E., and Yu, T. (2014). scikit-image: image processing in python. PeerJ, 2:e453.

Vanderborght, B., Simut, R., Saldien, J., Pop, C., Rusu, A. S., Pintea, S., Lefeber, D., and David, D. O. (2012). Using the social robot probo as a social story telling agent for children with asd. Interaction Studies, 13(3):348–372.

Verbeek, H., Buijs, J., Van Dongen, B., and van der Aalst, W. M. (2010). Prom 6: The process mining toolkit. Proc. of BPM Demonstration Track, 615:34–39.

Villamil, M. D. P., Barrera, D., Velasco, N., Bernal, O., Fajardo, E., Urango, C., and Buitrago, S. (2017). Strategies for the quality assessment of the health care service providers in the treatment of Gastric Cancer in Colombia. BMC Health Services Research, 17(1).

Wada, K., Shibata, T., Musha, T., and Kimura, S. (2008). Robot therapy for elders affected by dementia. IEEE Engineering in medicine and biology magazine, 27(4):53–60.

Wang, H. and Lin, Z. (2007). A novel algorithm for counting all common subsequences. In Proceedings of the 2007 IEEE International Conference on Granular Computing, GRC ’07, page 502, USA. IEEE Computer Society.

Wang, T., Tian, X., Yu, M., Qi, X., and Yang, L. (2017). Stage division and pattern discovery of complex patient care processes. Journal of Systems Science and Complexity, 30(5):1136–1159.

Wang, W., Xu, Y., Gao, R., Lu, R., Han, K., Wu, G., and Tan, W. (2020). Detection of SARS-CoV-2 in Different Types of Clinical Specimens. JAMA, 323(18):1843–1844.

Weijters, A., van Der Aalst,W. M., and De Medeiros, A. A. (2006). Process mining with the heuristics miner-algorithm. Technische Universiteit Eindhoven, Tech. Rep. WP, 166:1–34.

Weiskopf, N. G. and Weng, C. (2013). Methods and dimensions of electronic health record data quality assessment: Enabling reuse for clinical research. Journal of the American Medical Informatics Association, 20(1):144–151.

WHO - World Health Organization. (2019) “WHO Strategic Meeting on the Social Determinants of Health – final meeting summary”, Geneva, [link].

WHO - World Health Organization. Executive Board. (2021) “EB148/24 Social determinants of health: Report by the Director-General”, https://apps.who.int/gb/ebwha/pdf_files/EB148/B148_24-en.pdf.

Williams, R.D., Markus, A.F., Yang, C. et al. “Seek COVER: using a disease proxy to rapidly develop and validate a personalized risk calculator for COVID-19 outcomes in an international network.” BMC Med Res Methodol 22, 35 (2022). https://doi.org/10.1186/s12874-022-01505-z

Woods, D., Yuan, F., Jao, Y.-L., and Zhao, X. (2021). Social robots for older adults with dementia: A narrative review on challenges & future directions. In International Conference on Social Robotics, pages 411–420. Springer.

World Health Organization. Fifty-eighth world health assembly. World Health Assembly, WHA58/2005/REC/1, 2005.

World Health Organization. Monitoring and evaluating digital health interventions: a practical guide to conducting research and assessment. 2016.

X. Liu, S. Cruz Rivera, D. Moher, M. J. Calvert, and A. K. Denniston. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the consort-ai extension. Nat Med, 26(9):1364–1374, 2020.

Xiao,W., Li, M., Chen, M., and Barnawi, A. (2020). Deep interaction: Wearable robot-assisted emotion communication for enhancing perception and expression ability of children with autism spectrum disorders. Future Generation Computer Systems, 108:709–716.

Xu, X., Jin, T., Wei, Z., and Wang, J. (2017). Incorporating Topic Assignment Constraint and Topic Correlation Limitation into Clinical Goal Discovering for Clinical Pathway Mining. Journal of Healthcare Engineering, 2017.

Xu, X., Jin, T., Wei, Z., Lv, C., and Wang, J. (2016). TCPM: Topic-Based Clinical Pathway Mining. Proceedings - 2016 IEEE 1st International Conference on Connected Health: Applications, Systems and Engineering Technologies, CHASE 2016, pages 292–301.

Yabuki, K. and Sumi, K. (2018). Learning support system for effectively conversing with individuals with autism using a humanoid robot. In 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pages 4266–4270. IEEE.

Yang, G.-Z., Bellingham, J., Dupont, P. E., Fischer, P., Floridi, L., Full, R., Jacobstein, N., Kumar, V., McNutt, M., Merrifield, R., et al. (2018). The grand challenges of science robotics. Science robotics, 3(14):eaar7650.

Yang, W. S. and Hwang, S. Y. (2006). A process-mining framework for the detection of healthcare fraud and abuse. Expert Systems with Applications, 31(1):56–68.

Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. Deep learning. nature, 521(7553):436–444, 2015.

Yoo, S., Cho, M., Kim, E., Kim, S., Sim, Y., Yoo, D., Hwang, H., and Song, M. (2016). Assessment of hospital processes using a process mining technique: Outpatient process analysis at a tertiary hospital. International Journal of Medical Informatics, 88:34–43.

Yoo, S., Cho, M., Kim, S., Kim, E., Park, S. M., Kim, K., Hwang, H., and Song, M. (2015). Conformance analysis of clinical pathway using electronic health record data. Healthcare Informatics Research, 21(3):161–166.

Zaballa, O., Pérez, A., Inhiesto, E. G., Ayesta, T. A., and Lozano, J. A. (2020). Identifying common treatments from Electronic Health Records with missing information. An application to breast cancer. PLoS ONE, 15(12 December).

Zamboni, L.M. and Martin, E. G. (2020) “Distributing Local Resources for Public Health Preparedness Grants: A Data-Driven Approach”, Journal of Public Health Management and Practice, [link].

Zeeshan Ahmed, Khalid Mohamed, Saman Zeeshan, and XinQi Dong. Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine. Database : the journal of biological databases and curation, 2020:baaa010, 2020.

Zeng, Z., Tung, A. K., Wang, J., Feng, J., and Zhou, L. (2009). Comparing stars: On approximating graph edit distance. Proceedings of the VLDB Endowment, 2(1):25–36.

Zhang, X., Wang, L., Miao, S., Xu, H., Yin, Y., Zhu, Y., Dai, Z., Shan, T., Jing, S., Wang, J., Zhang, X., Huang, Z., Wang, Z., Guo, J., and Liu, Y. (2018). Analysis of treatment pathways for three chronic diseases using OMOP CDM. Journal of Medical Systems, 42(12).

Zhang, Y. and Padman, R. (2015). Innovations in Chronic Care Delivery Using Data-Driven Clinical Pathways. The American journal of managed care, 21(12):661–668.

Zhang, Y., Padman, R., and Patel, N. (2015a). Paving the COWpath: Learning and visualizing clinical pathways from electronic health record data. Journal of Biomedical Informatics, 58:186–197.

Zhang, Y., Padman, R., Wasserman, L., Patel, N., Teredesai, P., and Xie, Q. (2015b). On clinical pathway discovery from electronic health record data. IEEE Intelligent Systems, 30(1):70–75.

Zhao, Q., Wang, H., and Wang, G. (2021). Lcov-net: A lightweight neural network for covid-19 pneumonia lesion segmentation from 3d ct images. In 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pages 42–45.

Zhi-Hua Zhou. A brief introduction to weakly supervised learning. National science review, 5(1):44–53, 2018.

Zhou, T., Lu, H., Yang, Z., Qiu, S., Huo, B., and Dong, Y. (2021). The ensemble deep learning model for novel covid-19 on ct images. Applied Soft Computing, 98:106885.

Zhu, Z., Yin, C., Qian, B., Cheng, Y., Wei, J., and Wang, F. (2016). Measuring patient similarities via a deep architecture with medical concept embedding. In 2016 IEEE 16th International Conference on Data Mining (ICDM), pages 749–758.

Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie. A convnet for the 2020s. arXiv preprint arXiv:2201.03545, 2022.

Zoran Milanović, Saša Pantelić, Nebojša Trajković, Goran Sporiš, Radmila Kostić, and Nic James. Age-related decrease in physical activity and functional fitness among elderly men and women. Clinical interventions in aging, 8:549–556, 2013.