Data from: Learning relevance models for patient cohort retrieval

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 (EHR) collections. METHODS A very
large number of features were extracted from patient cohort descriptions
as well as electronic health record collections. Specifically, we
investigated retrieving (1) neurology-specific patient cohorts from the
Temple University Hospital 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 relevance. CONCLUSION The learning patient cohort retrieval
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 will continue to
learn, thus offering a potential solution to the performance barriers of
current cohort retrieval systems.