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Natural language processing of lifestyle modification documentation.

Kimberly Shoenbill1, Yiqiang Song, Lisa Gress

  • 1University of North Carolina at Chapel Hill, USA.

Health Informatics Journal
|February 23, 2019
PubMed
Summary
This summary is machine-generated.

This study uses natural language processing to extract lifestyle modification details from electronic health records. This method effectively converts textual health data into a usable format for research and improving patient care.

Keywords:
electronic health recordshealth behaviorhypertensionlifestylenatural language processingtext mining

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Area of Science:

  • Health Informatics
  • Natural Language Processing
  • Clinical Research

Background:

  • Lifestyle modifications (diet, exercise, smoking cessation) are crucial for managing chronic conditions like hypertension, obesity, and diabetes.
  • The textual nature of lifestyle modification data in electronic health records hinders its use in research and quality improvement.
  • There is a need for automated methods to extract and structure this valuable clinical information.

Purpose of the Study:

  • To develop and evaluate a natural language processing (NLP) approach for automatically identifying and classifying lifestyle modification (LM) information from electronic health records (EHRs).
  • To transform unstructured narrative LM data into a structured, coded format suitable for research and clinical quality improvement.

Main Methods:

  • Utilized an open-source NLP tool to analyze EHR notes from hypertension patients.
  • Extracted text pertaining to lifestyle modification assessment and advice.
  • Classified extracted data as either assessment or advice and mapped it to a standard ontology.

Main Results:

  • Achieved high performance in combined lifestyle modification (advice and assessment) extraction with 99.27% recall and 94.44% precision.
  • Demonstrated 88.15% accuracy in correctly classifying the extracted lifestyle modification data.
  • Successfully transformed narrative LM data into a coded format.

Conclusions:

  • Automated NLP methods can effectively extract and structure critical lifestyle modification data from EHRs.
  • This approach facilitates the use of LM data for research, metric development, and quality improvement initiatives.
  • The findings support the integration of NLP tools for enhancing the utility of clinical documentation in managing chronic diseases.