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An automatic system to identify heart disease risk factors in clinical texts over time

Chen, Qingcai ; Li, Haodi ; Tang, Buzhou ; Wang, Xiaolong ; Liu, Xin ; Liu, Zengjian ; Liu, Shu ; Wang, Weida ; Deng, Qiwen ; Zhu, Suisong ; Chen, Yangxin ; Wang, Jingfeng

Journal of biomedical informatics, 2015-12, Vol.58 (Suppl), p.S158-S163 [Periódico revisado por pares]

United States: Elsevier Inc

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Título:
An automatic system to identify heart disease risk factors in clinical texts over time
Autor: Chen, Qingcai ; Li, Haodi ; Tang, Buzhou ; Wang, Xiaolong ; Liu, Xin ; Liu, Zengjian ; Liu, Shu ; Wang, Weida ; Deng, Qiwen ; Zhu, Suisong ; Chen, Yangxin ; Wang, Jingfeng
Assuntos: Aged ; Biology ; Cardiovascular Diseases - diagnosis ; Cardiovascular Diseases - epidemiology ; China - epidemiology ; Clinical information extraction ; Cohort Studies ; Comorbidity ; Computational linguistics ; Computer Security ; Confidentiality ; Data Mining - methods ; Development and progression ; Diabetes Complications - diagnosis ; Diabetes Complications - epidemiology ; Electronic Health Records - organization & administration ; Female ; Heart disease ; Heart diseases ; Humans ; Incidence ; Language processing ; Longitudinal Studies ; Machine learning ; Male ; Medical ; Medical records ; Medical research ; Medicine, Experimental ; Middle Aged ; Narration ; Natural language interfaces ; Natural Language Processing ; Patients ; Pattern Recognition, Automated - methods ; Prevention ; Progressions ; Rankings ; Risk analysis ; Risk Assessment - methods ; Risk factor identification ; Risk factors ; Texts ; Tracking ; Vocabulary, Controlled
É parte de: Journal of biomedical informatics, 2015-12, Vol.58 (Suppl), p.S158-S163
Notas: ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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Descrição: [Display omitted] •We proposed a hybrid system to automatically identify heart disease risk factors.•We divided different types of risk factors into three categories according to their descriptions.•Our system achieves an F-score of 92.86% on 2014 i2b2 corpus, which is top-ranked. Despite recent progress in prediction and prevention, heart disease remains a leading cause of death. One preliminary step in heart disease prediction and prevention is risk factor identification. Many studies have been proposed to identify risk factors associated with heart disease; however, none have attempted to identify all risk factors. In 2014, the National Center of Informatics for Integrating Biology and Beside (i2b2) issued a clinical natural language processing (NLP) challenge that involved a track (track 2) for identifying heart disease risk factors in clinical texts over time. This track aimed to identify medically relevant information related to heart disease risk and track the progression over sets of longitudinal patient medical records. Identification of tags and attributes associated with disease presence and progression, risk factors, and medications in patient medical history were required. Our participation led to development of a hybrid pipeline system based on both machine learning-based and rule-based approaches. Evaluation using the challenge corpus revealed that our system achieved an F1-score of 92.68%, making it the top-ranked system (without additional annotations) of the 2014 i2b2 clinical NLP challenge.
Editor: United States: Elsevier Inc
Idioma: Inglês

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An automatic system to identify heart disease risk factors in clinical texts over time

Chen, Qingcai ; Li, Haodi ; Tang, Buzhou ; Wang, Xiaolong ; Liu, Xin ; Liu, Zengjian ; Liu, Shu ; Wang, Weida ; Deng, Qiwen ; Zhu, Suisong ; Chen, Yangxin ; Wang, Jingfeng

United States: Elsevier Inc

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