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Learning relevance models for patient cohort retrieval.

Travis R Goodwin1, Sanda M Harabagiu1

  • 1Department of Computer Science, Human Language Technology Research Institute, University of Texas at Dallas, Richardson, Texas, USA.

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Summary

Physician feedback enhances patient cohort retrieval from electronic health records (EHRs). The learning patient cohort retrieval (L-PCR) system demonstrated significant improvements, outperforming traditional methods in retrieving specific and general patient cohorts.

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Area of Science:

  • Medical Informatics
  • Health Data Science

Background:

  • Electronic Health Records (EHRs) contain vast patient data.
  • Retrieving specific patient cohorts from EHRs is challenging for research and clinical care.
  • Current retrieval methods often lack the precision needed for complex cohort identification.

Purpose of the Study:

  • To develop and evaluate a novel system for learning relevance models from physician judgments to improve patient cohort retrieval from EHRs.
  • To enhance the quality and precision of patient cohort identification using machine learning techniques.

Main Methods:

  • Extracted numerous features from patient cohort descriptions and EHR data.
  • Developed a learning relevance model (LRM) using a pairwise learning-to-rank framework.
  • Implemented a learning patient cohort retrieval (L-PCR) system trained on physician relevance judgments.

Main Results:

  • The L-PCR system achieved a 27% improvement in retrieving neurology-specific patient cohorts (EEG Corpus).
  • The L-PCR system showed a 53% improvement in retrieving general patient cohorts (TRECMed EHRs).
  • Feature analysis identified optimal strategies for query representation, EHR encoding, and relevance measurement.

Conclusions:

  • The L-PCR system demonstrates significant promise for reliable patient cohort retrieval from EHRs across different settings.
  • The system's ability to continuously learn from physician feedback offers a solution to current retrieval performance limitations.
  • Physician-guided learning is a key factor in improving the accuracy of EHR-based patient cohort discovery.