title: Case-based reasoning using electronic health records efficiently identifies eligible patients for clinical trials.
abstract: OBJECTIVE
  To develop a cost-effective, case-based reasoning framework for clinical research eligibility screening by only reusing the electronic health records (EHRs) of minimal enrolled participants to represent the target patient for each trial under consideration.
  
  
  METHODS
  The EHR data--specifically diagnosis, medications, laboratory results, and clinical notes--of known clinical trial participants were aggregated to profile the "target patient" for a trial, which was used to discover new eligible patients for that trial. The EHR data of unseen patients were matched to this "target patient" to determine their relevance to the trial; the higher the relevance, the more likely the patient was eligible. Relevance scores were a weighted linear combination of cosine similarities computed over individual EHR data types. For evaluation, we identified 262 participants of 13 diversified clinical trials conducted at Columbia University as our gold standard. We ran a 2-fold cross validation with half of the participants used for training and the other half used for testing along with other 30 000 patients selected at random from our clinical database. We performed binary classification and ranking experiments.
  
  
  RESULTS
  The overall area under the ROC curve for classification was 0.95, enabling the highlight of eligible patients with good precision. Ranking showed satisfactory results especially at the top of the recommended list, with each trial having at least one eligible patient in the top five positions.
  
  
  CONCLUSIONS
  This relevance-based method can potentially be used to identify eligible patients for clinical trials by processing patient EHR data alone without parsing free-text eligibility criteria, and shows promise of efficient "case-based reasoning" modeled only on minimal trial participants.
authors.affiliation: Department of Biomedical Informatics.
authors.forename: Riccardo
authors.initials: R
authors.lastname: Miotto
authors.affiliation: Department of Biomedical Informatics The Irving Institute for Clinical and Translational Research, Columbia University, New York, NY 10032, USA cw2384@cumc.columbia.edu.
authors.forename: Chunhua
authors.initials: C
authors.lastname: Weng
mesh_terms: D001185:Artificial Intelligence; D057286:Electronic Health Records; D004608:Eligibility Determination; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D018579:Patient Selection; D012372:ROC Curve; D016032:Randomized Controlled Trials as Topic
keywords: artificial intelligence; clinical trials; electronic health records; information storage and retrieval