Causality patterns and machine learning for the extraction of problem-action relations in discharge summaries.
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 clinic...
| Publicado en: | International Journal of Medical Informatics Vol. 98; pp. 1 - 13 |
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| Autores principales: | , , , |
| Formato: | research Journal Article |
| Publicado: |
Elsevier B.V.
Feb2017
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=120409966&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 120409966 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13865056 JR4 jtl: International Journal of Medical Informatics issn: 13865056 maglogo: N pubinfo: dt: Feb2017 vid: 98 pid: 467 pub: Elsevier B.V. place: New York, New York artinfo: ui: 120409966 120409966 NLM28034407 120409966 10.1016/j.ijmedinf.2016.10.021 NLM28034407 120409966 ppf: 1 ppct: 12 formats: tig: atl: Causality patterns and machine learning for the extraction of problem-action relations in discharge summaries. aug: au: Seol, Jae-Wook Yi, Wangjin Choi, Jinwook Lee, Kyung Soon affil: Department of Information Convergence Research, Korea Institute of Science and Technology Information 245, Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea sug: subj: Patient Discharge Standards Decision Support Systems, Clinical Natural Language Processing Semantics Narratives Information Science ab: 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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