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Family history information extraction via deep joint learning

Shi, Xue ; Jiang, Dehuan ; Huang, Yuanhang ; Wang, Xiaolong ; Chen, Qingcai ; Yan, Jun ; Tang, Buzhou

BMC medical informatics and decision making, 2019-12, Vol.19 (Suppl 10), p.277-277, Article 277 [Periódico revisado por pares]

England: BioMed Central Ltd

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Título:
Family history information extraction via deep joint learning
Autor: Shi, Xue ; Jiang, Dehuan ; Huang, Yuanhang ; Wang, Xiaolong ; Chen, Qingcai ; Yan, Jun ; Tang, Buzhou
Assuntos: Algorithms ; Clinical Decision-Making ; Computational Biology ; Computers ; Decision making ; Deep joint learning ; Deep Learning ; Electronic Health Records ; Electronic medical records ; Electronic records ; Entity identification ; Family ; Family history extraction ; Family history information ; Family medical history ; Genetics ; Health informatics ; Humans ; Information processing ; Information retrieval ; Information Storage and Retrieval - methods ; Information systems ; Learning ; Machine learning ; Medical diagnosis ; Medical History Taking ; Medical records ; Natural Language Processing ; Neural networks ; Support vector machines ; Teaching methods ; Unstructured data
É parte de: BMC medical informatics and decision making, 2019-12, Vol.19 (Suppl 10), p.277-277, Article 277
Notas: ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
Descrição: 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.
Editor: England: BioMed Central Ltd
Idioma: Inglês

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Family history information extraction via deep joint learning

Shi, Xue ; Jiang, Dehuan ; Huang, Yuanhang ; Wang, Xiaolong ; Chen, Qingcai ; Yan, Jun ; Tang, Buzhou

England: BioMed Central Ltd

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