Learning relevance models for patient cohort retrieval.

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 pr

SUBMITTER: Goodwin TR 

PROVIDER: S-EPMC6241510 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature