Relational machine learning for electronic health record-driven phenotyping.
Objective: Electronic health records (EHR) offer medical and pharmacogenomics research unprecedented opportunities to identify and classify patients at risk. EHRs are collections of highly inter-dependent records that include biological, anatomical, physiological, and behavioral observations. They c...
| Publicado en: | Journal of Biomedical Informatics Vol. 52; pp. 260 - 271 |
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| Autores principales: | , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Academic Press Inc.
Dec2014
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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=109768433&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 109768433 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15320464 OMB jtl: Journal of Biomedical Informatics issn: 15320464 maglogo: N pubinfo: dt: Dec2014 vid: 52 pid: 735 pub: Academic Press Inc. place: Burlington, Massachusetts artinfo: ui: 109768433 NLM25048351 2012824386 10.1016/j.jbi.2014.07.007 NLM25048351 PMC4261015 109768433 ppf: 260 ppct: 11 formats: tig: atl: Relational machine learning for electronic health record-driven phenotyping. aug: au: Peissig, Peggy L Santos Costa, Vitor Caldwell, Michael D Rottscheit, Carla Berg, Richard L Mendonca, Eneida A Page, David sug: subj: Artificial Intelligence Data Mining Methods Electronic Health Records Classification Algorithms Resource Databases Human ab: Objective: Electronic health records (EHR) offer medical and pharmacogenomics research unprecedented opportunities to identify and classify patients at risk. EHRs are collections of highly inter-dependent records that include biological, anatomical, physiological, and behavioral observations. They comprise a patient's clinical phenome, where each patient has thousands of date-stamped records distributed across many relational tables. Development of EHR computer-based phenotyping algorithms require time and medical insight from clinical experts, who most often can only review a small patient subset representative of the total EHR records, to identify phenotype features. In this research we evaluate whether relational machine learning (ML) using inductive logic programming (ILP) can contribute to addressing these issues as a viable approach for EHR-based phenotyping.Methods: Two relational learning ILP approaches and three well-known WEKA (Waikato Environment for Knowledge Analysis) implementations of non-relational approaches (PART, J48, and JRIP) were used to develop models for nine phenotypes. International Classification of Diseases, Ninth Revision (ICD-9) coded EHR data were used to select training cohorts for the development of each phenotypic model. Accuracy, precision, recall, F-Measure, and Area Under the Receiver Operating Characteristic (AUROC) curve statistics were measured for each phenotypic model based on independent manually verified test cohorts. A two-sided binomial distribution test (sign test) compared the five ML approaches across phenotypes for statistical significance.Results: We developed an approach to automatically label training examples using ICD-9 diagnosis codes for the ML approaches being evaluated. Nine phenotypic models for each ML approach were evaluated, resulting in better overall model performance in AUROC using ILP when compared to PART (p=0.039), J48 (p=0.003) and JRIP (p=0.003).Discussion: ILP has the potential to improve phenotyping by independently delivering clinically expert interpretable rules for phenotype definitions, or intuitive phenotypes to assist experts.Conclusion: Relational learning using ILP offers a viable approach to EHR-driven phenotyping. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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