CBN: Constructing a clinical Bayesian network based on data from the electronic medical record.
The process of learning candidate causal relationships involving diseases and symptoms from electronic medical records (EMRs) is the first step towards learning models that perform diagnostic inference directly from real healthcare data. However, the existing diagnostic inference systems rely on kno...
| Published in: | Journal of Biomedical Informatics Vol. 88; pp. 1 - 11 |
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| Main Authors: | , , , , , , |
| Format: | research Journal Article |
| Published: |
Academic Press Inc.
Dec2018
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=133557508&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 133557508 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15320464 OMB jtl: Journal of Biomedical Informatics issn: 15320464 maglogo: N pubinfo: dt: Dec2018 vid: 88 pid: 735 pub: Academic Press Inc. place: Burlington, Massachusetts artinfo: ui: 133557508 133557508 NLM30399432 133557508 10.1016/j.jbi.2018.10.007 NLM30399432 133557508 ppf: 1 ppct: 10 formats: tig: atl: CBN: Constructing a clinical Bayesian network based on data from the electronic medical record. aug: au: Shen, Ying Zhang, Lizhu Zhang, Jin Yang, Min Tang, Buzhou Li, Yaliang Lei, Kai affil: ICNLAB, School of Electronics and Computer Engineering, Peking University Shenzhen Graduate School, 518055 Shenzhen, PR China sug: subj: Probability Medical Informatics Methods Human Odds Ratio Algorithms ROC Curve Data Collection Software Risk Factors Knowledge Bases False Positive Results Validation Studies Comparative Studies Evaluation Research Multicenter Studies ab: The process of learning candidate causal relationships involving diseases and symptoms from electronic medical records (EMRs) is the first step towards learning models that perform diagnostic inference directly from real healthcare data. However, the existing diagnostic inference systems rely on knowledge bases such as ontology that are manually compiled through a labour-intensive process or automatically derived using simple pairwise statistics. We explore CBN, a Clinical Bayesian Network construction for medical ontology probabilistic inference, to learn high-quality Bayesian topology and complete ontology directly from EMRs. Specifically, we first extract medical entity relationships from over 10,000 deidentified patient records and adopt the odds ratio (OR value) calculation and the K2 greedy algorithm to automatically construct a Bayesian topology. Then, Bayesian estimation is used for the probability distribution. Finally, we employ a Bayesian network to complete the causal relationship and probability distribution of ontology to enhance the ontology inference capability. By evaluating the learned topology versus the expert opinions of physicians and entropy calculations and by calculating the ontology-based diagnosis classification, our study demonstrates that the direct and automated construction of a high-quality health topology and ontology from medical records is feasible. Our results are reproducible, and we will release the source code and CN-Stroke knowledge graph of this work after publication.1. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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