title: Causality patterns and machine learning for the extraction of problem-action relations in discharge summaries.
abstract: Clinical narrative text includes information related to a patient's medical history such as chronological progression of medical problems and clinical treatments. A chronological view of a patient's history makes clinical audits easier and improves quality of care. In this paper, we propose a clinical Problem-Action relation extraction method, based on clinical semantic units and event causality patterns, to present a chronological view of a patient's problem and a doctor's action. Based on our observation that a clinical text describes a patient's medical problems and a doctor's treatments in chronological order, a clinical semantic unit is defined as a problem and/or an action relation. Since a clinical event is a basic unit of the problem and action relation, events are extracted from narrative texts, based on the external knowledge resources context features of the conditional random fields. A clinical semantic unit is extracted from each sentence based on time expressions and context structures of events. Then, a clinical semantic unit is classified into a problem and/or action relation based on the event causality patterns of the support vector machines. Experimental results on Korean discharge summaries show 78.8% performance in the F1-measure. This result shows that the proposed method is effectively classifies clinical Problem-Action relations.
authors.affiliation: Department of Information Convergence Research, Korea Institute of Science and Technology Information 245, Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea. Electronic address: wodnr754@kisti.re.kr.
authors.forename: Jae-Wook
authors.initials: JW
authors.lastname: Seol
authors.affiliation: Interdisciplinary Program of Bioengineering, College of Engineering, Seoul National University 103, Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea. Electronic address: jinsamdol@snu.ac.kr.
authors.forename: Wangjin
authors.initials: W
authors.lastname: Yi
authors.affiliation: Dept. of Biomedical Engineering, College of Medicine, Seoul National University 103, Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea. Electronic address: jinchoi@snu.ac.kr.
authors.forename: Jinwook
authors.initials: J
authors.lastname: Choi
authors.affiliation: Department of Computer Engineering, CAIIT, Chonbuk National University 567, Baekjedae-ro, Deokjin-gu, Jeonju, Jeollabukdo, 54896, Republic of Korea. Electronic address: selfsolee@chonbuk.ac.kr.
authors.forename: Kyung Soon
authors.initials: KS
authors.lastname: Lee
mesh_terms: D020000:Decision Support Systems, Clinical; D057286:Electronic Health Records / Q000706:statistics & numerical data*; D006801:Humans; D000069550:Machine Learning; D033262:Narration; D009323:Natural Language Processing; D010351:Patient Discharge / Q000592:standards*; D010363:Pattern Recognition, Automated; D012660:Semantics; D060388:Support Vector Machine
keywords: Causality pattern; Clinical semantic unit; Machine learning; Problem-Action relation; Relation extraction