title: Relational machine learning for electronic health record-driven phenotyping.
abstract: 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).
  
  
  CONCLUSIONS
  ILP has the potential to improve phenotyping by independently delivering clinically expert interpretable rules for phenotype definitions, or intuitive phenotypes to assist experts.
  
  
  CONCLUSIONS
  Relational learning using ILP offers a viable approach to EHR-driven phenotyping.
authors.affiliation: Biomedical Informatics Research Center, Marshfield Clinic Research Foundation, Marshfield, WI, USA. Electronic address: peissig.peggy@marshfieldclinic.org.
authors.forename: Peggy L
authors.initials: PL
authors.lastname: Peissig
authors.affiliation: DCC-FCUP and CRACS INESC-TEC, Department de Ciência de Computadores, Universidade do Porto, Portugal.
authors.forename: Vitor
authors.initials: V
authors.lastname: Santos Costa
authors.affiliation: Department of Surgery, Marshfield Clinic, Marshfield, WI, USA.
authors.forename: Michael D
authors.initials: MD
authors.lastname: Caldwell
authors.affiliation: Biomedical Informatics Research Center, Marshfield Clinic Research Foundation, Marshfield, WI, USA.
authors.forename: Carla
authors.initials: C
authors.lastname: Rottscheit
authors.affiliation: Biomedical Informatics Research Center, Marshfield Clinic Research Foundation, Marshfield, WI, USA.
authors.forename: Richard L
authors.initials: RL
authors.lastname: Berg
authors.affiliation: Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, USA; Department of Pediatrics, University of Wisconsin-Madison, USA.
authors.forename: Eneida A
authors.initials: EA
authors.lastname: Mendonca
authors.affiliation: Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, USA; Department of Computer Sciences, University of Wisconsin-Madison, USA.
authors.forename: David
authors.initials: D
authors.lastname: Page
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D057225:Data Mining / Q000379:methods*; D016208:Databases, Factual; D057286:Electronic Health Records / Q000145:classification*; D006801:Humans
keywords: Electronic health record; Inductive logic programming; Machine learning; Phenotyping; Relational machine learning