Um Modelo para Predição Individualizada da Deterioração da Saúde de Pacientes no âmbito da Internet das Coisas
Resumo
Este artigo apresenta o desenvolvimento de um modelo baseado em internet das coisas de saúde e inteligência artificial para prever a deterioração do estado de saúde de pacientes. Foi construído um modelo de Deep Learning baseado em Redes Neurais Convolucionais para processar os dados que podem ser coletados por sensores ubíquos. Foram utilizadas duas estratégias diferentes para treinar o modelo, uma baseada na abordagem populacional tradicional e outra mais individualizada onde os dados de treinamento são oriundos de pacientes similares em termos de idade ao indivíduo monitorado. Os resultados mostraram uma sensível melhora na acurácia da predição de mortalidade do modelo individualizado com AUC média de 0,73 para todas as faixas de idade enquanto o modelo populacional apresentou AUC de 0,70.
Referências
Alaa, A. M., Yoon, J., Hu, S., and van der Schaar, M. (2018). Personalized risk scoring for critical care prognosis using mixtures of gaussian processes. IEEE Transactions on Biomedical Engineering, 65(1):207–218.
Angermueller, C., Parnamaa, T., Parts, L., and Stegle, O. (2016).¨ Deep learning for computational biology. Molecular systems biology, 12(7):878.
Barbour, D. L. (2019). Precision medicine and the cursed dimensions. npj Digital Medicine, 2(1).
Boness, C. L., Loeffelman, J. E., Steinley, D., Trull, T., and Sher, K. J. (2020). Using complete enumeration to derive “one-size-fits-all” versus “subgroup-specific” diagnostic rules for substance use disorder. Assessment.
Brajer, N., Cozzi, B., Gao, M., Nichols, M., Revoir, M., Balu, S., Futoma, J., Bae, J., Setji, N., Hernandez, A., and Sendak, M. (2020). Prospective and external evaluation of a machine learning model to predict in-hospital mortality of adults at time of admission. JAMA Network Open, 3(2).
Caicedo-Torres, W. and Gutierrez, J. (2019). Iseeu: Visually interpretable deep learning for mortality prediction inside the icu. Journal of Biomedical Informatics, 98(103269).
Caulfield, B., Reginatto, B., and Slevin, P. (2019). Not all sensors are created equal: a framework for evaluating human performance measurement technologies. npj Digital Medicine, 2.
Cooper, J. N., Minneci, P. C., and Deans, K. J. (2018). Postoperative neonatal mortality prediction using superlearning. Journal of Surgical Research, 221:311–319.
da Costa, C. A., Pasluosta, C. F., Eskofier, B., da Silva, D. B., and da Rosa Righi, R. (2018). Internet of health things: Toward intelligent vital signs monitoring in hospital wards. Artificial Intelligence in Medicine, 89:61 – 69.
Ding, Y., Wang, Y., and Zhou, D. (2018). Mortality prediction for icu patients combining just-in-time learning and extreme learning machine. Neurocomputing, 281:12–19.
dos Santos, H. G., Zampieri, F. G., Normilio-Silva, K., da Silva, G. T., de Lima, A. C. P., Cavalcanti, A. B., and Filho, A. D. P. C. (2020). Machine learning to predict 30-day quality-adjusted survival in critically ill patients with cancer. Journal of Critical Care, 55:73–78.
Ershoff, B. D., Lee, C. K., Wray, C. L., Agopian, V. G., Urban, G., Baldi, P., and Cannesson, M. (2020). Training and validation of deep neural networks for the prediction of 90-day post-liver transplant mortality using unos registry data. Transplantation Proceedings, 52(1):246–258.
Gianfrancesco, M. A., Tamang, S., Yazdany, J., and Schmajuk, G. (2018). Potential Biases in Machine Learning Algorithms Using Electronic Health Record Data. JAMA Internal Medicine, 178(11):1544–1547.
Gombar, S., Callahan, A., Califf, R., Harrington, R., and Shah, N. H. (2019). It is time to learn from patients like mine. npj Digital Medicine, 2.
Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning. MIT Press.
Haykin, S. (2007). Redes neurais: princípios e pratica . Bookman, Porto Alegre.
He, J., Baxter, S. L., Xu, J., Xu, J., and Zhou, X. (2019). The practical implementation of artificial intelligence technologies in medicine. Nature Medicine, 25(1).
Ho, K., Lee, K., Williams, T., Finn, J., Knuiman, M., and Webb, S. (2007). Comparison of acute physiology and chronic health evaluation (apache) ii score with organ failure scores to predict hospital mortality. Anaesthesia, 62(5):466–473.
Innocenti, F., Tozzi, C., Donnini, C., Villa, E. D., Conti, A., Zanobetti, M., and Pini, R. (2018). Sofa score in septic patients: incremental prognostic value over age, comorbidities, and parameters of sepsis severity. Internal and Emergency Medicine, 13.
Johnson, A. E., Pollard, T. J., Shen, L., wei H. Lehman, L., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Celi, L. A., and Mark, R. G. (2016). MIMIC-III, a freely accessible critical care database. Scientific Data, 3. Article number: 160035.
Kim, H. J., Kim, H. J., Park, Y., Lee, W. S., Lim, Y., and Kim, J. H. (2020). Clinical genome data model (cgdm) provides interactive clinical decision support for precision medicine. Scientific Reports, 10(1):1414.
Knaus, W. A., Draper, E. A., Wagnet, D. P., and Zimmennan, J. E. (1985). APACHE II: a severity of disease classification system. Critical care medicine, 13(10):818–829.
Krishnan, G. S. and S., S. K. (2019). A novel ga-elm model for patient-specific mortality prediction over large-scale lab event data. Applied Soft Computing, 80:525–533.
LeCun, Y., Bengio, Y., and Hinton, G. (2015). Deep learning. Nature, 521(7553).
Lin, K., Hu, Y., and Kong, G. (2019). Predicting in-hospital mortality of patients with acute kidney injury in the icu using random forest model. International Journal of Medical Informatics, 125:55–61.
Ma, X., Si, Y., Wang, Z., and Wang, Y. (2020). Length of stay prediction for icu patients using individualized single classification algorithm. Computer Methods and Programs in Biomedicine, 186.
Mansoor, H., Elgendy, I. Y., Segal, R., Bavry, A. A., and Bian, J. (2017). Risk prediction model for in-hospital mortality in women with st-elevation myocardial infarction: A machine learning approach. Heart & Lung, 46(6):405–411.
Martha, S. R., Auld, J. P., Hash, J. B., and Hong, H. (2020). Precision health in aging and nursing practice. Journal of Gerontological Nursing, 46(3):3–6.
Metnitz, P. G. H., Moreno, R. P., Almeida, E., Jordan, B., Bauer, P., Campos, R. A., Iapichino, G., Edbrooke, D., Capuzzo, M., and Gall, J.-R. L. (2005). Saps 3–from evaluation of the patient to evaluation of the intensive care unit. part 2: Development of a prognostic model for hospital mortality at icu admission. Intensive care medicine, 31(10):1345–1355.
Mills, J. R. (2017). Precision medicine—right treatment, right patient, right time, wrong approach? Clinical Chemistry, 63(4).
Nicolas, P. R. (2017). Scala for Machine Learning: Data processing, ML algorithms, smart analytics, and more. Packt Publishing Ltd, Birmingham, UK, second edition.
Obermeyer, Z., Powers, B., Vogeli, C., and Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464):447–453.
Qi, Y., Wang, W., Zhang, K., An, S., Wang, S., Zheng, J., and Yi-DaTang (2018). Development and validation of women acute myocardial infarction in-hospital mortality score (wami score). International Journal of Cardiology, 259:31–39.
Royal College of Physicians (2017). National early warning score (news) 2: Standardising the assessment of acute-illness severity in the NHS. Technical report, Royal College of Physicians, London.
Schmidt, D., da Silva, D. B., da Costa, C. A., and da Rosa Righi, R. (2018). Um modelo de predição de mortalidade em unidades de terapia intensiva baseado em deep learning. In Simposio Brasileiro de Computacao Aplicada a Saude (SBCAS 2018) . SBC.
Shamout, F., Zhu, T., Clifton, L., Briggs, J., Prytherch, D., Meredith, P., Tarassenko, L., Watkinson, P. J., and Clifton, D. A. (2019). Early warning score adjusted for age to predict the composite outcome of mortality, cardiac arrest or unplanned intensive care unit admission using observational vital-sign data: a multicentre development and validation. BMJ open, 9(11).
Shen, X. (2020). An imprecise path to precision medicine. Nature Medicine, 26(1):14–14.
Thorsen-Meyer, H.-C., Nielsen, A. B., Nielsen, A. P., Kaas-Hansen, B. S., Toft, P., Schierbeck, J., Strøm, T., Chmura, P. J., Heimann, M., Dybdahl, L., Spangsege, L., Hulsen, P., Belling, K., Brunak, S., and Perner, A. (2020). Dynamic and explainable machine learning prediction of mortality in patients in the intensive care unit: a retrospective study of high-frequency data in electronic patient records. The Lancet Digital Health.
Todd, J., Gepp, A., Richards, B., and Vanstone, B. J. (2019). Improving mortality models in the icu with high-frequency data. International Journal of Medical Informatics, 129:318–323.
van Loon, I. N., Bots, M. L., Boereboom, F. T. J., Grooteman, M. P. C., Blankestijn, P. J., van den Dorpel, M. A., Nube, M. J., Ter Wee, P. M., Verhaar, M. C., and Hamaker, M. E. (2017). Quality of life as indicator of poor outcome in hemodialysis: relation with mortality in different age groups. BMC nephrology, 18(1):217–217.
Zimmennan, J. E., Kramer, A. A., McNair, D. S., and Malila, F. M. (2006). Acute physiology and chronic health evaluation (apache) iv: hospital mortality assessment for today’s critically ill patients. Critical care medicine, 34(5):1297–1310.
Angermueller, C., Parnamaa, T., Parts, L., and Stegle, O. (2016).¨ Deep learning for computational biology. Molecular systems biology, 12(7):878.
Barbour, D. L. (2019). Precision medicine and the cursed dimensions. npj Digital Medicine, 2(1).
Boness, C. L., Loeffelman, J. E., Steinley, D., Trull, T., and Sher, K. J. (2020). Using complete enumeration to derive “one-size-fits-all” versus “subgroup-specific” diagnostic rules for substance use disorder. Assessment.
Brajer, N., Cozzi, B., Gao, M., Nichols, M., Revoir, M., Balu, S., Futoma, J., Bae, J., Setji, N., Hernandez, A., and Sendak, M. (2020). Prospective and external evaluation of a machine learning model to predict in-hospital mortality of adults at time of admission. JAMA Network Open, 3(2).
Caicedo-Torres, W. and Gutierrez, J. (2019). Iseeu: Visually interpretable deep learning for mortality prediction inside the icu. Journal of Biomedical Informatics, 98(103269).
Caulfield, B., Reginatto, B., and Slevin, P. (2019). Not all sensors are created equal: a framework for evaluating human performance measurement technologies. npj Digital Medicine, 2.
Cooper, J. N., Minneci, P. C., and Deans, K. J. (2018). Postoperative neonatal mortality prediction using superlearning. Journal of Surgical Research, 221:311–319.
da Costa, C. A., Pasluosta, C. F., Eskofier, B., da Silva, D. B., and da Rosa Righi, R. (2018). Internet of health things: Toward intelligent vital signs monitoring in hospital wards. Artificial Intelligence in Medicine, 89:61 – 69.
Ding, Y., Wang, Y., and Zhou, D. (2018). Mortality prediction for icu patients combining just-in-time learning and extreme learning machine. Neurocomputing, 281:12–19.
dos Santos, H. G., Zampieri, F. G., Normilio-Silva, K., da Silva, G. T., de Lima, A. C. P., Cavalcanti, A. B., and Filho, A. D. P. C. (2020). Machine learning to predict 30-day quality-adjusted survival in critically ill patients with cancer. Journal of Critical Care, 55:73–78.
Ershoff, B. D., Lee, C. K., Wray, C. L., Agopian, V. G., Urban, G., Baldi, P., and Cannesson, M. (2020). Training and validation of deep neural networks for the prediction of 90-day post-liver transplant mortality using unos registry data. Transplantation Proceedings, 52(1):246–258.
Gianfrancesco, M. A., Tamang, S., Yazdany, J., and Schmajuk, G. (2018). Potential Biases in Machine Learning Algorithms Using Electronic Health Record Data. JAMA Internal Medicine, 178(11):1544–1547.
Gombar, S., Callahan, A., Califf, R., Harrington, R., and Shah, N. H. (2019). It is time to learn from patients like mine. npj Digital Medicine, 2.
Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning. MIT Press.
Haykin, S. (2007). Redes neurais: princípios e pratica . Bookman, Porto Alegre.
He, J., Baxter, S. L., Xu, J., Xu, J., and Zhou, X. (2019). The practical implementation of artificial intelligence technologies in medicine. Nature Medicine, 25(1).
Ho, K., Lee, K., Williams, T., Finn, J., Knuiman, M., and Webb, S. (2007). Comparison of acute physiology and chronic health evaluation (apache) ii score with organ failure scores to predict hospital mortality. Anaesthesia, 62(5):466–473.
Innocenti, F., Tozzi, C., Donnini, C., Villa, E. D., Conti, A., Zanobetti, M., and Pini, R. (2018). Sofa score in septic patients: incremental prognostic value over age, comorbidities, and parameters of sepsis severity. Internal and Emergency Medicine, 13.
Johnson, A. E., Pollard, T. J., Shen, L., wei H. Lehman, L., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Celi, L. A., and Mark, R. G. (2016). MIMIC-III, a freely accessible critical care database. Scientific Data, 3. Article number: 160035.
Kim, H. J., Kim, H. J., Park, Y., Lee, W. S., Lim, Y., and Kim, J. H. (2020). Clinical genome data model (cgdm) provides interactive clinical decision support for precision medicine. Scientific Reports, 10(1):1414.
Knaus, W. A., Draper, E. A., Wagnet, D. P., and Zimmennan, J. E. (1985). APACHE II: a severity of disease classification system. Critical care medicine, 13(10):818–829.
Krishnan, G. S. and S., S. K. (2019). A novel ga-elm model for patient-specific mortality prediction over large-scale lab event data. Applied Soft Computing, 80:525–533.
LeCun, Y., Bengio, Y., and Hinton, G. (2015). Deep learning. Nature, 521(7553).
Lin, K., Hu, Y., and Kong, G. (2019). Predicting in-hospital mortality of patients with acute kidney injury in the icu using random forest model. International Journal of Medical Informatics, 125:55–61.
Ma, X., Si, Y., Wang, Z., and Wang, Y. (2020). Length of stay prediction for icu patients using individualized single classification algorithm. Computer Methods and Programs in Biomedicine, 186.
Mansoor, H., Elgendy, I. Y., Segal, R., Bavry, A. A., and Bian, J. (2017). Risk prediction model for in-hospital mortality in women with st-elevation myocardial infarction: A machine learning approach. Heart & Lung, 46(6):405–411.
Martha, S. R., Auld, J. P., Hash, J. B., and Hong, H. (2020). Precision health in aging and nursing practice. Journal of Gerontological Nursing, 46(3):3–6.
Metnitz, P. G. H., Moreno, R. P., Almeida, E., Jordan, B., Bauer, P., Campos, R. A., Iapichino, G., Edbrooke, D., Capuzzo, M., and Gall, J.-R. L. (2005). Saps 3–from evaluation of the patient to evaluation of the intensive care unit. part 2: Development of a prognostic model for hospital mortality at icu admission. Intensive care medicine, 31(10):1345–1355.
Mills, J. R. (2017). Precision medicine—right treatment, right patient, right time, wrong approach? Clinical Chemistry, 63(4).
Nicolas, P. R. (2017). Scala for Machine Learning: Data processing, ML algorithms, smart analytics, and more. Packt Publishing Ltd, Birmingham, UK, second edition.
Obermeyer, Z., Powers, B., Vogeli, C., and Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464):447–453.
Qi, Y., Wang, W., Zhang, K., An, S., Wang, S., Zheng, J., and Yi-DaTang (2018). Development and validation of women acute myocardial infarction in-hospital mortality score (wami score). International Journal of Cardiology, 259:31–39.
Royal College of Physicians (2017). National early warning score (news) 2: Standardising the assessment of acute-illness severity in the NHS. Technical report, Royal College of Physicians, London.
Schmidt, D., da Silva, D. B., da Costa, C. A., and da Rosa Righi, R. (2018). Um modelo de predição de mortalidade em unidades de terapia intensiva baseado em deep learning. In Simposio Brasileiro de Computacao Aplicada a Saude (SBCAS 2018) . SBC.
Shamout, F., Zhu, T., Clifton, L., Briggs, J., Prytherch, D., Meredith, P., Tarassenko, L., Watkinson, P. J., and Clifton, D. A. (2019). Early warning score adjusted for age to predict the composite outcome of mortality, cardiac arrest or unplanned intensive care unit admission using observational vital-sign data: a multicentre development and validation. BMJ open, 9(11).
Shen, X. (2020). An imprecise path to precision medicine. Nature Medicine, 26(1):14–14.
Thorsen-Meyer, H.-C., Nielsen, A. B., Nielsen, A. P., Kaas-Hansen, B. S., Toft, P., Schierbeck, J., Strøm, T., Chmura, P. J., Heimann, M., Dybdahl, L., Spangsege, L., Hulsen, P., Belling, K., Brunak, S., and Perner, A. (2020). Dynamic and explainable machine learning prediction of mortality in patients in the intensive care unit: a retrospective study of high-frequency data in electronic patient records. The Lancet Digital Health.
Todd, J., Gepp, A., Richards, B., and Vanstone, B. J. (2019). Improving mortality models in the icu with high-frequency data. International Journal of Medical Informatics, 129:318–323.
van Loon, I. N., Bots, M. L., Boereboom, F. T. J., Grooteman, M. P. C., Blankestijn, P. J., van den Dorpel, M. A., Nube, M. J., Ter Wee, P. M., Verhaar, M. C., and Hamaker, M. E. (2017). Quality of life as indicator of poor outcome in hemodialysis: relation with mortality in different age groups. BMC nephrology, 18(1):217–217.
Zimmennan, J. E., Kramer, A. A., McNair, D. S., and Malila, F. M. (2006). Acute physiology and chronic health evaluation (apache) iv: hospital mortality assessment for today’s critically ill patients. Critical care medicine, 34(5):1297–1310.
Publicado
15/09/2020
Como Citar
KAIESKI, Naira; SCHMIDT, Diogo; KELSH, Carolina; SILVA, Denise; COSTA, Cristiano; RIGHI, Rodrigo .
Um Modelo para Predição Individualizada da Deterioração da Saúde de Pacientes no âmbito da Internet das Coisas. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 20. , 2020, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 167-178.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2020.11511.
