Family history information extraction via deep joint learning.

Clicks: 220
ID: 85157
2019
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Ranked #43 of 65 articles by views in BMC medical informatics and decision making

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Abstract
Family history (FH) information, including family members, side of family of family members (i.e., maternal or paternal), living status of family members, observations (diseases) of family members, etc., is very important in the decision-making process of disorder diagnosis and treatment. However FH information cannot be used directly by computers as it is always embedded in unstructured text in electronic health records (EHRs). In order to extract FH information form clinical text, there is a need of natural language processing (NLP). In the BioCreative/OHNLP2018 challenge, there is a task regarding FH extraction (i.e., task1), including two subtasks: (1) entity identification, identifying family members and their observations (diseases) mentioned in clinical text; (2) family history extraction, extracting side of family of family members, living status of family members, and observations of family members. For this task, we propose a system based on deep joint learning methods to extract FH information. Our system achieves the highest F1- scores of 0.8901 on subtask1 and 0.6359 on subtask2, respectively.
Reference Key
shi2019familybmc Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Shi, Xue;Jiang, Dehuan;Huang, Yuanhang;Wang, Xiaolong;Chen, Qingcai;Yan, Jun;Tang, Buzhou;
Journal BMC medical informatics and decision making
Year 2019
DOI
10.1186/s12911-019-0995-5
URL
Keywords
deep joint learning entity identification family history extraction family history information

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