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Construction of cardiovascular information extraction corpus based on electronic medical records
Hongyang Chang1, Hongying Zan1,2, Shuai Zhang1
1School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou, China.
Insights
Researchers created a new dataset of cardiovascular disease (CVD) electronic medical records. This Cardiovascular Disease Electronic Medical Record Entity and Relationship Labeling Corpus (CVDEMRC) will advance knowledge-based research and information extraction in the field.
Area of Science:
- Biomedical Informatics
- Cardiovascular Research
- Natural Language Processing
Background:
- Cardiovascular disease (CVD) poses significant societal and patient burdens.
- Knowledge-based research, including knowledge graphs and automated question answering, is crucial for advancing CVD understanding.
- Limited availability of specialized corpora hinders knowledge-based research in cardiovascular disease.
Purpose of the Study:
- To address the scarcity of cardiovascular disease research resources.
- To construct a comprehensive corpus from electronic medical records (EMRs) for cardiovascular disease.
- To facilitate knowledge extraction and information retrieval in cardiovascular disease research.
Main Methods:
- Collected electronic medical record data specific to cardiovascular disease.
- Developed a standardized methodology for labeling cardiovascular EMR entities and their relationships.
- Utilized a rule-based, semi-automatic approach to build a sentence-level labeling dictionary.
- Constructed the Cardiovascular Disease Electronic Medical Record Entity and Relationship Labeling Corpus (CVDEMRC).
Main Results:
- The CVDEMRC contains 7,691 labeled entities and 11,185 entity-relationship triples.
- Achieved high consistency rates: 93.51% for entity annotations and 84.02% for entity-relationship annotations.
- Demonstrated the feasibility and reliability of the developed labeling standards and methods.
Conclusions:
- The constructed CVDEMRC provides a valuable, high-quality database for cardiovascular disease information extraction.
- This corpus is expected to significantly support and advance knowledge-based research in cardiovascular disease.
- The study highlights the potential of leveraging EMRs for creating specialized biomedical corpora.
Abstract:
Cardiovascular disease has a significant impact on both society and patients, making it necessary to conduct knowledge-based research such as research that utilizes knowledge graphs and automated question answering. However, the existing research on corpus construction for cardiovascular disease is relatively limited, which has hindered further knowledge-based research on this disease. Electronic medical records contain patient data that span the entire diagnosis and treatment process and include a large amount of reliable medical information. Therefore, we collected electronic medical record data related to cardiovascular disease, combined the data with relevant work experience and developed a standard for labeling cardiovascular electronic medical record entities and entity relations. By building a sentence-level labeling result dictionary through the use of a rule-based semi-automatic method, a cardiovascular electronic medical record entity and entity relationship labeling corpus (CVDEMRC) was constructed. The CVDEMRC contains 7691 entities and 11,185 entity relation triples, and the results of consistency examination were 93.51% and 84.02% for entities and entity-relationship annotations, respectively, demonstrating good consistency results. The CVDEMRC constructed in this study is expected to provide a database for information extraction research related to cardiovascular diseases.
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