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Clinical Annotation Research Kit (CLARK): Computable Phenotyping Using Machine Learning.

Emily R Pfaff1, Miles Crosskey2, Kenneth Morton2

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Summary
This summary is machine-generated.

Clinical Annotation Research Kit (CLARK) is an open-source software enabling researchers to use machine learning-based natural language processing (NLP) for computable phenotyping without needing informatics expertise. CLARK facilitates mining electronic health record (EHR) text data for richer patient cohort definitions.

Keywords:
electronic health recordsmachine learningnatural language processing

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Area of Science:

  • Biomedical Informatics
  • Clinical Research
  • Natural Language Processing

Background:

  • Computable phenotypes derived from structured electronic health record (EHR) data lack comprehensive patient information.
  • Natural language processing (NLP) methods for extracting clinical features from text are typically limited to experts.
  • Bridging the gap between NLP expertise and clinical research is crucial for advancing computable phenotyping.

Purpose of the Study:

  • To develop an open-source software, Clinical Annotation Research Kit (CLARK), empowering clinical and translational researchers to perform machine learning-based NLP for computable phenotyping.
  • To enable nonexpert users to leverage NLP tools for defining patient cohorts using EHR data without extensive informatics knowledge.

Main Methods:

  • CLARK software allows users to define features for machine learning classifiers to identify in clinical notes.
  • The user-friendly interface supports various machine learning algorithms (SVM, Naïve Bayes, decision trees, random forests) and cross-validation techniques.
  • CLARK facilitates the incorporation of unstructured text data into computable phenotype algorithms.

Main Results:

  • CLARK has been successfully applied to define phenotypes such as pediatric diabetes, symptomatic uterine fibroids, nonalcoholic fatty liver disease, and primary ciliary dyskinesia.
  • Performance metrics for CLARK applications include high sensitivity and specificity for several example phenotypes.
  • CLARK enables the inclusion of variables not available in structured EHR data, enhancing phenotype accuracy.

Conclusions:

  • CLARK democratizes the use of machine learning-based NLP for computable phenotyping, making it accessible to researchers without specialized informatics skills.
  • The software significantly improves the status quo by lowering the barrier to entry for advanced NLP techniques in clinical research.
  • Dissemination of CLARK aims to enable wider adoption of NLP-driven phenotyping across institutions lacking dedicated NLP or machine learning experts.