Case-based reasoning using electronic health records efficiently identifies eligible patients for clinical trials

TitleCase-based reasoning using electronic health records efficiently identifies eligible patients for clinical trials
Publication TypeJournal Article
Year of Publication2015
AuthorsMiotto R, Weng C
JournalJournal of the American Medical Informatics Association
Start Pagee141
Date Published03/2015
Keywordsartificial intelligence, clinical trials, electronic health records, information storage and retrieval

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.
Materials and 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.

Refereed DesignationUnknown