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ABSTRACT: Background
Electronic health records store large amounts of patient clinical data. Despite efforts to structure patient data, clinical notes containing rich patient information remain stored as free text, greatly limiting its exploitation. This includes family history, which is highly relevant for applications such as diagnosis and prognosis.Objective
This study aims to develop automatic strategies for annotating family history information in clinical notes, focusing not only on the extraction of relevant entities such as family members and disease mentions but also on the extraction of relations between the identified entities.Methods
This study extends a previous contribution for the 2019 track on family history extraction from national natural language processing
SUBMITTER: Silva JF
PROVIDER: S-EPMC7803476 | biostudies-literature | 2020 Dec
REPOSITORIES: biostudies-literature