Ummodelo de anonimização aplicado a prontuários eletrônicos 268 MUSEN, M. A. The protégé project: a look back and a look forward. AI matters, Nova York, v. 1, n. 4, p. 4-12, 2015. NÉVÉOL, A.; ZWEIGENBAUM, P. et al. Making sense of big textual data for health care: findings from the section on clinical natural language processing. Yearbook of medical informatics, Stuttgart, v. 26, n. 1, p. 228-234, 2017. NGIAM, K. Y.; KHOR, I. W. Big data and machine learning algorithms for health-care delivery. The Lancet Oncology, Londres, v. 20, n. 5, p. e262-e273, 2019. PERER, A.; WANG, F.; HU, J. Mining and exploring care pathways from electronic medical records with visual analytics. Journal of biomedical informatics, Amsterdã, v. 56, p. 369-378, 2015. PERLIS, R. et al. Using electronic medical records to enable largescale studies in psychiatry: treatment resistant depression as a model. Psychological medicine, Cambridge, Reino Unido, v. 42, n. 1, p. 41-50, 2012. RASMY, L. et al. CovRNN – A recurrent neural network model for predicting outcomes of COVID-19 patients: model development and validation using EHR data. The Lancet Digital Health, v. 4, n. 6, p. e415-e425, 29 set. 2021. RASMY, L. et al. Med-bert: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction. NPJ digital medicine, Londres, v. 4, n. 1, p. 1-13, 2021. RAZZAQUE, A.; HAMDAN, A. Artificial intelligence based multinational corporate model for EHR interoperability on an E-health platform. In: HASSANIEN, Aboul Ella; BHATNAGAR, Roheet; DARWISH, Ashraf. Artificial intelligence for sustainable development: theory, practice and future applications. Cham: Springer, 2021. p. 71-81. RUDRAPATNA, V. A.; BUTTE, A. J. Opportunities and challenges in using real-world data for health care. The Journal of Clinical Investigation, v. 130, n. 2, p. 565-574, fev. 2020.
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