Uso de rotinas de aprendizado de máquina em prontuário eletrônico para apoio a diagnósticos de pacientes oftalmológicos

• Ano de defesa: 2021
• Autor(a) principal: Alves, Lucas De Oliveira Batista [UNIFESP]
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de São Paulo (UNIFESP)
• Palavras-chave em Português: Artificial Intelligence; Machine Learning; Electronic Medical Record; Diagnostic Support; Inteligência Artificial; Aprendizado De Máquina; Prontuário Eletrônico; Apoio Diagnóstico
• Link de acesso: https://sucupira.capes.gov.br/sucupira/public/consultas/coleta/trabalhoConclusao/viewTrabalhoConclusao.jsf?popup=true&id_trabalho=10989782; https://repositorio.unifesp.br/handle/11600/68367

Objective: To implement artificial intelligence routines through machine learning to construct diagnostic prediction models with data from electronic medical records of patients from the Department of Ophthalmology of Hospital São Paulo. Method: Preparation of a literature review of the main techniques and solutions of machine learning to use in electronic medical records, 1. extraction, treatment and analysis of data from medical records of the Department; 2. construction and analysis of vectorization models of related words in the context of the Database of Hospital São Paulo; 3. construction and validation of diagnostic prediction models. Results: The word vectorization models were able to capture the semantics of medical terms and enabled the construction of diagnostic prediction models, making the prediction model a great tool to assist health professionals. Conclusion: The machine learning models showed potential results to assist as diagnostic support tools of ophthalmologic patients.