Ummodelo de anonimização aplicado a prontuários eletrônicos 266 CHOI, E. et al. Multi-layer representation learning for medical concepts. In: ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, 22., 2016, San Francisco. Anais [...]. Nova York: ACM, 2016. p. 1495-1504. CHOI, E. et al. Using recurrent neural network models for early detection of heart failure onset. Journal of the American Medical Informatics Association, Oxford, Reino Unido, v. 24, n. 2, p. 361-370, 2017. DEMNER-FUSHMAN, D.; CHAPMAN, W. W.; MCDONALD, C. J. What can natural language processing do for clinical decision support? Journal of biomedical informatics, Oxford, Reino Unido, v. 42, n. 5, p. 760-772, 2009. DHOLE, G.; UKE, N. Nlp based retrieval of medical information for diagnosis of human diseases. Int J Renew Energy Technol, v. 3, n. 10, p. 243e8, 2014. DUAN, L.; STREET, W. N.; XU, E. Healthcare information systems: data mining methods in the creation of a clinical recommender system. Enterprise Information Systems, Abingdon, Oxfordshire, Reino Unido, v. 5, n. 2, p. 169-181, 2011. ESTEVA, A. et al. A guide to deep learning in healthcare. Naturemedicine, Londres, v. 25, n. 1, p. 24-29, 2019. FORD, E. et al. Extracting information from the text of electronic medical records to improve case detection: a systematic review. Journal of the American Medical Informatics Association, Oxford, v. 23, n. 5, p. 1007-1015, 2016. GOLDSTEIN, B. A. et al. Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review. Journal of the American Medical Informatics Association, Oxford, v. 24, n. 1, p. 198-208, 2017. HEART, T.; BEN-ASSULI, O.; SHABTAI, I. A review of phr, emr and ehr integration: a more personalized healthcare and public health policy. Health Policy and Technology, Londres, v. 6, n. 1, p. 20-25, 2017. HOCHREITER, S.; SCHMIDHUBER, J. Long short-termmemory. Neural computation, Cambridge, Massachusetts EUA, v. 9, n. 8, p. 1735-1780, 1997.
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