Developing a cardiovascular disease risk factor annotated corpus of Chinese electronic medical records

Jia Su1, Bin He1, Yi Guan2

  • 1Language Technology Research Center, Harbin Institute of Technology, School of Computer Science and Technology, No. 92 West Dazhi Street, Harbin, Heilongjiang, China.

Insights

This study created the first annotated corpus for cardiovascular disease (CVD) risk factors in Chinese electronic medical records (CEMRs). This resource aids in developing systems to track CVD risk factors and disease progression.

Area of Science:

  • Medical Informatics
  • Public Health
  • Data Science

Background:

  • Cardiovascular disease (CVD) is the leading cause of death in China, with preventable risk factors.
  • Controlling CVD risk factors is crucial for public health in China.

Purpose of the Study:

  • To develop an annotated corpus of CVD risk factors from Chinese electronic medical records (CEMRs).
  • To establish a foundation for an information extraction system to study CVD risk factor progression.

Main Methods:

  • Designed a light annotation task for CVD risk factors, including indicators, temporal attributes, and assertions.
  • Developed annotation guidelines with clinician input, trained annotators, and constructed the corpus.
  • Incorporated under-threshold values, possible/negative risk factors, and temporal attributes for comprehensive analysis.

Main Results:

  • Created a reliable annotated corpus of CVD risk factors from 600 de-identified CEMRs.
  • Achieved a high inter-annotator agreement (IAA) F1-measure of 0.968, demonstrating annotation quality.
  • The corpus includes unique annotations for under-threshold values, assertions, and temporal variations.

Conclusions:

  • This is the first annotated corpus and guideline set for CVD risk factors in CEMRs.
  • The developed annotations enable long-term monitoring of risk factors and CVD progression.
  • The corpus serves as a valuable resource for future research in CVD and electronic health record analysis.
Abstract

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