Exploring Heterogeneous Data Processing to Improve Clinical Applications
Resumo
Os sistemas de computadores têm sido amplamente utilizados na melhoria da qualidade dos serviços de saúde. Em geral, esses sistemas não suportam diferentes formatos de dados e isso consiste em uma limitação severa. Este artigo apresenta um modelo que possibilita o uso de diferentes formatos de dados para fornecer e integrar informações que apoiam as atividades de médicos especialistas. Dois protótipos foram construídos usando o modelo, que visa exemplificar seus benefícios e permitir sua avaliação. Para avaliar a abordagem proposta, além das aplicações desenvolvidas, doze profissionais de saúde e 35 profissionais de informática responderam a um questionário sobre os protótipos. Os dois perfis distintos dos participantes do questionário e o desenvolvimento dos protótipos permitiram avaliar as contribuições percebidas nas áreas de desenvolvimento de software e aplicação de suporte clínico.
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El-Dahshan ESA, Mohsen HM, Revett K, Salem ABM. Computer-aideddiagnosis of human brain tumor through MRI: A survey and a new algorithm. Expert Systems with Applications 2014;41(11):5526–5545.
Bozkurt S, Gimenez F, Burnside ES, Gulkesen KH, Rubin DL. Using automatically extracted information from mammography reports for decision support. Journal of Biomedical Informatics 2016;62:224–231.
Pons E, Braun LMM, Hunink MGM, Kors JA. Natural Language Processing in Radiology: A Systematic Review. Radiology 2016;.
Topaz M, Lai K, Dowding D, Lei VJ, Zisberg A, Bowles KH, Zhou L. Automated identification of wound information in clinical notes of patients with heart diseases. International Journal of Nursing Studies 2016;64:25–31.
Cheng JZ, Ni D, Chou YH, Qin J, Tiu CM, Chang YC, Huang CS, Shen D,Chen CM. Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans. Scientific Reports 2016;6.
Firmino M, Angelo G, Morais H, Dantas MR, Valentim R. Computeraided detection (CADe) and diagnosis (CADx) system for lung cancer with likelihood of malignancy. BioMedical Engineering Online 2016;15(1).
Williams M, Wu F, Kazanzides P, Brady K, Fackler J. A modular framework for clinical decision support systems. ACM SIGBED Review. 2009;6(2):1–11.
Puppala M, He T, Chen S, Ogunti R, Yu X, Li F, Jackson R, WongSTC. METEOR: An Enterprise Health Informatics Environment to Support Evidence-Based Medicine. IEEE Transactions on Biomedical Engineering 2015;62(12):2776–2786.
Sylvestre E, Bouzillé G, Chazard E, His-Mahier C. .Combining information from a clinical data warehouse and a pharmaceutical database to generate a framework to detect comorbidities in electronic health records. BMC Medical Informatics and Decision Making 2018;.
Maglogiannis I, Goudas T, Billiris A, Karanikas H, Valavanis I, PapadodimaO, Kontogianni G, Chatziioannou A. Redesigning EHRs and Clinical Decision Support Systems for the Precision Medicine Era. In: Proceedings of the 16th International Conference on Engineering Applications of Neural Networks (INNS) - EANN ’15. ISBN 978-1-4503-3580-5; 2015:.
Weese J, Lorenz C. Four challenges in medical image analysis from anindustrial perspective. Medical Image Analysis 2016;33:44–49.
Kansagra AP, Yu JPJ, Chatterjee AR, Lenchik L, Chow DS, Prater AB,Yeh J, Doshi AM, Hawkins CM, Heilbrun ME, Smith SE, Oselkin M, Gupta P, Ali S. Big Data and the Future of Radiology Informatics. Academic Radiology 2016;23(1):30–42.
Morrison JJ, Hostetter JM, Aggarwal A, Filice RW. Constructing a Computer-Aided Differential Diagnosis Engine from Open-Source APIs. Journal of Digital Imaging 2016;29(6):654–657.
Zhao J, Papapetrou P, Asker L, Boström H. Learning from heterogeneoustemporal data in electronic health records. Journal of Biomedical Informatics 2017;65:105–119.
Pampouchidou A, Marias K, Yang F, Tsiknakis M, Pediaditis M, Manousos G, Meriaudeau F, Simos P. Depression Assessment by Fusing High and Low Level Features from Audio, Video, and Text. In: AVEC ’16. New York,: ACM Press. 2016:27–34.
Wen H, Liu Y, Rekik I, Wang S, Chen Z, Zhang J, Zhang Y, Peng Y, He H. Multi-modal multiple kernel learning for accurate identification of Tourette syndrome children. Pattern Recognition 2017;63:601–611.
Hazlehurst BL, Kurtz SE, Masica A, Stevens VJ, McBurnie MA, PuroJE, Vijayadeva V, Au DH, Brannon ED, Sittig DF. CER Hub: An informatics platform for conducting comparative effectiveness research using multi-institutional, heterogeneous, electronic clinical data. International Journal of Medical Informatics 2015;84(10):763–773.
Mourão A, Martins F, Magalhães J. Multimodal medical information retrieval with unsupervised rank fusion. Computerized Medical Imaging and Graphics 2015;39:35–45.
Vizza P, Guzzi PH, Veltri P, Cascini GL, Curia R, Sisca L. GIDAC: A prototype for bioimages annotation and clinical data integration. In: 2016 IEEE International Conference on Bioinformatics and Biomedicine. ISBN 978-1-5090-1611-2; 2016:1028–1031.
Riegler M, Lux M, Griwodz C, Spampinato C, de Lange T, Eskeland SL,Pogorelov K, Tavanapong W, Schmidt PT, Gurrin C, Johansen D, Johansen H, Halvorsen P. Multimedia and Medicine: Teammates for Better Disease Detection and Survival. Proceedings of the 2016 ACM on Multimedia Conference 2016;:968–977.
Allwell-Brown E. A Comparative Analysis of HL7 FHIR and openEHRfor Electronic Aggregation, Exchange and Reuse of Patient Data in Acute Care. Ph.D. thesis; Stockholm University; 2016.
Cai T, Giannopoulos AA, Yu S, Kelil T, Ripley B, Kumamaru KK, RybickiFJ, Mitsouras D. Natural Language Processing Technologies in Radiology Research and Clinical Applications. RadioGraphics 2016;36(1):176–191.
Aarhus: Aarhus Reis A, Rönnau RF, Pohren N, Spaniol AR, Becker J, Perez Â, Bez MR,de Oliveira RF. Ferramenta para apoio ao diagnóstico baseada em processamento de imagens de tomografia computadorizada do tórax. IV ERCASEscola Regional de Computação Aplicada à Saúde 2016;:20–21.
Sund-Levander M, Forsberg C, Wahren LK. Normal oral, rectal, tympanicand axillary body temperature in adult men and women: A systematic literature review. Scandinavian Journal of Caring Sciences 2002;16(2):122– 128.
Davis FD. Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly 1989;.
Venkatesh V, Davis FD. A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science. 2000.
Likert R. A technique for the measurement of attitudes. Archives of Psychology 1932.
Publicado
15/09/2020
Como Citar
RONNAU, Rodrigo; RIGO, Sandro; BEZ, Marta; BARBOSA, Jorge.
Exploring Heterogeneous Data Processing to Improve Clinical Applications. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 20. , 2020, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 108-119.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2020.11506.
