Steven Bedrick

Test collections for electronic health record-based clinical information retrieval

Yanshan Wang, Andrew Wen, Sijia Liu, William Hersh, Steven Bedrick, Hongfang Liu
JAMIA Open, Oct 2019


Objectives: To create test collections for evaluating clinical information retrieval (IR) systems and advancing clinical IR research. Materials and Methods: Electronic health record (EHR) data, including structured and free-text data, from 45 000 patients who are a part of the Mayo Clinic Biobank cohort was retrieved from the clinical data warehouse. The clinical IR system indexed a total of 42 million free-text EHR documents. The search queries consisted of 56 topics developed through a collaboration between Mayo Clinic and Oregon Health & Science University. We described the creation of test collections, including a to-be-evaluated document pool using five retrieval models, and human assessment guidelines. We analyzed the relevance judgment results in terms of human agreement and time spent, and results of three levels of relevance, and reported performance of five retrieval models. Results: The two judges had a moderate overall agreement with a Kappa value of 0.49, spent a consistent amount of time judging the relevance, and were able to identify easy and difficult topics. The conventional retrieval model performed best on most topics while a concept-based retrieval model had better performance on the topics requiring conceptual level retrieval. Discussion: IR can provide an alternate approach to leveraging clinical narratives for patient information discovery as it is less dependent on semantics. Our study showed the feasibility of test collections along with a few challenges. Conclusion: The conventional test collections for evaluating the IR system show potential for successfully evaluating clinical IR systems with a few challenges to be investigated.

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