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  • 1Department of Medicine, Center for Biomedical Informatics Research, Stanford University, Stanford, CA.

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We developed a natural language processing (NLP) pipeline to automatically document digital rectal examination (DRE) findings in clinical notes. This NLP tool achieved high precision and recall, improving prostate cancer quality metric tracking.

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Area of Science:

  • Medical Informatics
  • Natural Language Processing
  • Oncology

Background:

  • Digital rectal examination (DRE) is a crucial quality metric in prostate cancer care.
  • DRE-related information is often unstructured, residing in free-text clinical narratives.
  • Accurate documentation of DRE is essential for quality assessment and patient care.

Purpose of the Study:

  • To develop and evaluate a natural language processing (NLP) pipeline for automated DRE documentation in clinical notes.
  • To improve the extraction of DRE information from unstructured text data.
  • To enhance the accuracy and efficiency of tracking a key prostate cancer quality metric.

Main Methods:

  • Creation of a domain-specific dictionary by clinical experts.
  • Expansion of the dictionary using distributional semantics algorithms on clinical notes.
  • Development of a rule-based NLP pipeline incorporating the expert and learned dictionaries.
  • Comparison of the proposed NLP pipeline against a baseline NLP algorithm.

Main Results:

  • The proposed NLP pipeline demonstrated superior performance compared to the baseline.
  • Achieved high precision (0.95) in documenting DRE findings.
  • Achieved high recall (0.90) in identifying DRE information within clinical narratives.

Conclusions:

  • The developed NLP pipeline accurately and efficiently identifies digital rectal examination (DRE) quality metrics.
  • Enriching rule-based NLP with corpus-learned terms enhances performance for clinical documentation.
  • This approach offers a robust solution for extracting critical DRE data from electronic health records.