Learning relevance models for patient cohort retrieval. Issue 2 (28th September 2018)

• Record Type: Journal Article
• Authors: Goodwin, Travis R; Harabagiu, Sanda M
• Is Part Of: JAMIA open. Volume 1:Issue 2(2018)
• Page Start: 265
• Page End: 275
• Publication Date: 2018-09-28
• Subjects: medical informatics -- information storage and retrieval -- search engine -- machine learning; Medical informatics -- Periodicals; 610.285
• DOI: 10.1093/jamiaopen/ooy010
• Languages: English
• ISSNs: 2574-2531

Abstract

Abstract: Objective: We explored how judgements provided by physicians can be used to learn relevance models that enhance the quality of patient cohorts retrieved from Electronic Health Records (EHRs) collections. Methods: A very large number of features were extracted from patient cohort descriptions as well as EHR collections. The features were used to investigate retrieving (1) neurology-specific patient cohorts from the de-identified Temple University Hospital electroencephalography (EEG) Corpus as well as (2) the more general cohorts evaluated in the TREC Medical Records Track (TRECMed) from the de-identified hospital records provided by the University of Pittsburgh Medical Center. The features informed a learning relevance model (LRM) that took advantage of relevance judgements provided by physicians. The LRM implements a pairwise learning-to-rank framework, which enables our learning patient cohort retrieval (L-PCR) system to learn from physicians' feedback. Results and Discussion: We evaluated the L-PCR system against state-of-the-art traditional patient cohort retrieval systems, and observed a 27% improvement when operating on EEGs and a 53% improvement when operating on TRECMed EHRs, showing the promise of the L-PCR system. We also performed extensive feature analyses to reveal the most effective strategies for representing cohort descriptions as queries, encoding EHRs, and measuring cohort relevance. Conclusion: The L-PCR system has significant promise for reliably retrieving patient cohorts from EHRs in multiple settings when trained with relevance judgments. When provided with additional cohort descriptions, the L-PCR system will continue to learn, thus offering a potential solution to the performance barriers of current cohort retrieval systems.