Natural Language Processing to Extract Information from Portuguese-Language Medical Records

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorFundação para o Desenvolvimento Médico e Hospitalar (FAMESP)-
Autor(es): dc.creatorda Rocha, Naila Camila-
Autor(es): dc.creatorBarbosa, Abner Macola Pacheco-
Autor(es): dc.creatorSchnr, Yaron Oliveira-
Autor(es): dc.creatorMachado-Rugolo, Juliana-
Autor(es): dc.creatorde Andrade, Luis Gustavo Modelli-
Autor(es): dc.creatorCorrente, José Eduardo-
Autor(es): dc.creatorde Arruda Silveira, Liciana Vaz-
Data de aceite: dc.date.accessioned2025-08-21T22:53:10Z-
Data de disponibilização: dc.date.available2025-08-21T22:53:10Z-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2022-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.3390/data8010011-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/246711-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/246711-
Descrição: dc.descriptionStudies that use medical records are often impeded due to the information presented in narrative fields. However, recent studies have used artificial intelligence to extract and process secondary health data from electronic medical records. The aim of this study was to develop a neural network that uses data from unstructured medical records to capture information regarding symptoms, diagnoses, medications, conditions, exams, and treatment. Data from 30,000 medical records of patients hospitalized in the Clinical Hospital of the Botucatu Medical School (HCFMB), São Paulo, Brazil, were obtained, creating a corpus with 1200 clinical texts. A natural language algorithm for text extraction and convolutional neural networks for pattern recognition were used to evaluate the model with goodness-of-fit indices. The results showed good accuracy, considering the complexity of the model, with an F-score of 63.9% and a precision of 72.7%. The patient condition class reached a precision of 90.3% and the medication class reached 87.5%. The proposed neural network will facilitate the detection of relationships between diseases and symptoms and prevalence and incidence, in addition to detecting the identification of clinical conditions, disease evolution, and the effects of prescribed medications.-
Descrição: dc.descriptionDepartment of Biostatistics Institute of Biosciences Universidade Estadual Paulista (UNESP)-
Descrição: dc.descriptionMedical School Universidade Estadual Paulista (UNESP)-
Descrição: dc.descriptionHealth Technology Assessment Center (Clinical Hospital of the Botucatu Medical School)-
Descrição: dc.descriptionResearch Support Office Fundação para o Desenvolvimento Médico e Hospitalar (FAMESP)-
Descrição: dc.descriptionDepartment of Biostatistics Institute of Biosciences Universidade Estadual Paulista (UNESP)-
Descrição: dc.descriptionMedical School Universidade Estadual Paulista (UNESP)-
Descrição: dc.descriptionHealth Technology Assessment Center (Clinical Hospital of the Botucatu Medical School)-
Idioma: dc.languageen-
Relação: dc.relationData-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectmedical records-
Palavras-chave: dc.subjectnamed entity recognition-
Palavras-chave: dc.subjectneural networks-
Título: dc.titleNatural Language Processing to Extract Information from Portuguese-Language Medical Records-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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