Relational machine learning for electronic health record-driven phenotyping

[Display omitted] •We compared ILP to propositional machine learning approaches for EHR phenotyping.•Training subject selection for machine learning was automated using ICD-9 codes.•ILP out-performed propositional machine learning approaches in AUROC.•Relational learning using ILP offers a viable ap...

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Veröffentlicht in:	Journal of biomedical informatics Jg. 52; S. 260 - 270
Hauptverfasser:	Peissig, Peggy L., Santos Costa, Vitor, Caldwell, Michael D., Rottscheit, Carla, Berg, Richard L., Mendonca, Eneida A., Page, David
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Elsevier Inc 01.12.2014
Schlagworte:	Algorithms Artificial Intelligence Classification Data Mining - methods Databases, Factual Electronic health record Electronic health records Electronic Health Records - classification Electronics Humans Inductive logic programming Learning Machine learning Medical Patients Phenotyping Relational machine learning Training Electronic health record Inductive logic programming Relational machine learning Machine learning Phenotyping
ISSN:	1532-0464, 1532-0480, 1532-0480
Online-Zugang:	Volltext
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