EMR-based medical knowledge representation and inference via Markov random fields and distributed representation learning.
Objective: Electronic medical records (EMRs) contain medical knowledge that can be used for clinical decision support (CDS). Our objective is to develop a general system that can extract and represent knowledge contained in EMRs to support three CDS tasks-test recommendation, initial diagnosis, and...
| Publicado en: | Artificial Intelligence in Medicine Vol. 87; pp. 49 - 60 |
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| Autores principales: | , , , , |
| Formato: | research Journal Article |
| Publicado: |
Elsevier B.V.
May2018
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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=129825967&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 129825967 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09333657 3HY jtl: Artificial Intelligence in Medicine issn: 09333657 maglogo: N pubinfo: dt: May2018 vid: 87 pid: 1004 pub: Elsevier B.V. artinfo: ui: 129825967 129825967 NLM29691122 129825967 10.1016/j.artmed.2018.03.005 NLM29691122 129825967 ppf: 49 ppct: 11 formats: tig: atl: EMR-based medical knowledge representation and inference via Markov random fields and distributed representation learning. aug: au: Zhao, Chao Jiang, Jingchi Guan, Yi Guo, Xitong He, Bin affil: School of Computer Science and Technology, Harbin, Heilongjiang 150001, China sug: subj: Decision Support Systems, Clinical Probability Algorithms Validation Studies Comparative Studies Evaluation Research Multicenter Studies Scales ab: Objective: Electronic medical records (EMRs) contain medical knowledge that can be used for clinical decision support (CDS). Our objective is to develop a general system that can extract and represent knowledge contained in EMRs to support three CDS tasks-test recommendation, initial diagnosis, and treatment plan recommendation-given the condition of a patient.Methods: We extracted four kinds of medical entities from records and constructed an EMR-based medical knowledge network (EMKN), in which nodes are entities and edges reflect their co-occurrence in a record. Three bipartite subgraphs (bigraphs) were extracted from the EMKN, one to support each task. One part of the bigraph was the given condition (e.g., symptoms), and the other was the condition to be inferred (e.g., diseases). Each bigraph was regarded as a Markov random field (MRF) to support the inference. We proposed three graph-based energy functions and three likelihood-based energy functions. Two of these functions are based on knowledge representation learning and can provide distributed representations of medical entities. Two EMR datasets and three metrics were utilized to evaluate the performance.Results: As a whole, the evaluation results indicate that the proposed system outperformed the baseline methods. The distributed representation of medical entities does reflect similarity relationships with respect to knowledge level.Conclusion: Combining EMKN and MRF is an effective approach for general medical knowledge representation and inference. Different tasks, however, require individually designed energy functions. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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