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Developing a cardiovascular disease risk factor annotated corpus of Chinese electronic medical records
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.
Background:
Cardiovascular disease (CVD) has become the leading cause of death in China, and most of the cases can be prevented by controlling risk factors. The goal of this study was to build a corpus of CVD risk factor annotations based on Chinese electronic medical records (CEMRs). This corpus is intended to be used to develop a risk factor information extraction system that, in turn, can be applied as a foundation for the further study of the progress of risk factors and CVD.
Results:
We designed a light annotation task to capture CVD risk factors with indicators, temporal attributes and assertions that were explicitly or implicitly displayed in the records. The task included: 1) preparing data; 2) creating guidelines for capturing annotations (these were created with the help of clinicians); 3) proposing an annotation method including building the guidelines draft, training the annotators and updating the guidelines, and corpus construction. Meanwhile, we proposed some creative annotation guidelines: (1) the under-threshold medical examination values were annotated for our purpose of studying the progress of risk factors and CVD; (2) possible and negative risk factors were concerned for the same reason, and we created assertions for annotations; (3) we added four temporal attributes to CVD risk factors in CEMRs for constructing long term variations. Then, a risk factor annotated corpus based on de-identified discharge summaries and progress notes from 600 patients was developed. Built with the help of clinicians, this corpus has an inter-annotator agreement (IAA) F1-measure of 0.968, indicating a high reliability.
Conclusion:
To the best of our knowledge, this is the first annotated corpus concerning CVD risk factors in CEMRs and the guidelines for capturing CVD risk factor annotations from CEMRs were proposed. The obtained document-level annotations can be applied in future studies to monitor risk factors and CVD over the long term.
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