Mining Patterns of Disease Progression: A Topic-Model-Based Approach.
Knowledge of how diseases progress and transform is crucial for clinical decision making. Frequent pattern mining techniques, such as sequential pattern mining (SPM) algorithms, can automatically extract such knowledge from large collections of electronic medical records (EMR). However, EMR data are...
| Publicado en: | Studies in Health Technology & Informatics Vol. 228; pp. 354 - 359 |
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| Autores principales: | , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2016
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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=117766195&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 117766195 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09269630 U1V jtl: Studies in Health Technology & Informatics issn: 09269630 maglogo: N pubinfo: dt: 2016 vid: 228 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 117766195 117766195 117766195 10.3233/978-1-61499-678-1-354 117766195 ppf: 354 ppct: 5 formats: tig: atl: Mining Patterns of Disease Progression: A Topic-Model-Based Approach. aug: au: ZHANG, Lingxiao ZHAO, Junfeng WANG, Yasha XIE, Bing affil: Software Institute, School of EECS, Peking University, Beijing, P.R. China sug: subj: Disease Progression Data Mining Utilization Decision Support Systems, Clinical Medical Informatics Electronic Health Records Human Time Factors Chi Square Test Diagnosis International Classification of Diseases Leukemia Funding Source Algorithms China ab: Knowledge of how diseases progress and transform is crucial for clinical decision making. Frequent pattern mining techniques, such as sequential pattern mining (SPM) algorithms, can automatically extract such knowledge from large collections of electronic medical records (EMR). However, EMR data are usually unorganized and highly noisy. Finding meaningful disease patterns often calls for manual manipulation such as cohort and feature selection on EMR data by medical professionals. In this paper, we propose a topic-model-based SPM approach to find disease progression patterns from diagnostic records. We improve the traditional SPM algorithms by filtering and grouping the diagnosis sequences according to different clinical topics. These topics represent certain clinical conditions with closely related diagnoses, and are detected without prior medical knowledge. The experiment on real-world EMR data shows that our approach is able to find meaningful progression patterns with less noises, and can help quickly identify interesting patterns related to a certain clinical condition with less human effort. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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