Utilizing soft constraints to enhance medical relation extraction from the history of present illness in electronic medical records.
Relation extraction between medical concepts from electronic medical records has pervasive applications as well as significance. However, previous researches utilizing machine learning algorithms judge the semantic types of medical concept pair mentions independently. In fact, different concept pair...
| Publicado en: | Journal of Biomedical Informatics Vol. 87; pp. 108 - 118 |
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| Autores principales: | , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Academic Press Inc.
Nov2018
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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=132992896&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 132992896 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15320464 OMB jtl: Journal of Biomedical Informatics issn: 15320464 maglogo: N pubinfo: dt: Nov2018 vid: 87 pid: 735 pub: Academic Press Inc. place: Burlington, Massachusetts artinfo: ui: 132992896 132992896 NLM30292854 132992896 10.1016/j.jbi.2018.09.013 NLM30292854 132992896 ppf: 108 ppct: 10 formats: tig: atl: Utilizing soft constraints to enhance medical relation extraction from the history of present illness in electronic medical records. aug: au: Chen, Li Li, Yuanju Chen, Weipeng Liu, Xinglong Yu, Zhonghua Zhang, Siyuan affil: Department of Computer Science, Sichuan University, Chengdu, China sug: subj: Medical Informatics Methods Probability Reproducibility of Results Regression Models, Statistical Human Algorithms China Validation Studies Comparative Studies Evaluation Research Multicenter Studies Clinical Assessment Tools ab: Relation extraction between medical concepts from electronic medical records has pervasive applications as well as significance. However, previous researches utilizing machine learning algorithms judge the semantic types of medical concept pair mentions independently. In fact, different concept pair mentions in the same context are of dependencies which can provide beneficial evidences for identifying their relation types. To the best of our knowledge, only one study has considered such dependencies in discharge summaries. However, its hard constraints are not applied effectively to the History of Present Illness (HPI) in electronic Medical Records. According to the writing characteristics of HPI records, we generalize two regularities of dependencies among concept pairs mentioned in an HPI record to enhance the performance of relation extraction. We incorporate the two soft constraints corresponding to the regularities and the posterior probabilities returned by a local classifier into a joint inference process which applies Integer Quadratic Programming method to carry out collective classification for all concept pair mentions in an HPI record. We implement four local classification models including support vector machine, logistics regression, random forest and piecewise convolutional neural networks to examine the performance of our approach. A series of experimental results demonstrate that our collective classification method has made a principal improvement and outperforms the other state-of-the-art methods. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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