Extracting principal diagnosis, co-morbidity and smoking status for asthma research: evaluation of a natural language processing system
Open accessAbstract
BACKGROUND: The text descriptions in electronic medical records are a rich source of information. We have developed a Health Information Text Extraction (HITEx) tool and used it to extract key findings for a research study on airways disease. METHODS: The principal diagnosis, co-morbidity and smoking status extracted by HITEx from a set of 150 discharge summaries were compared to an expert-generated gold standard. RESULTS: The accuracy of HITEx was 82% for principal diagnosis, 87% for co-morbidity, and 90% for smoking status extraction, when cases labeled "Insufficient Data" by the gold standard were excluded. CONCLUSION: We consider the results promising, given the complexity of the discharge summaries and the extraction tasks.
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- Authors
- Zeng, Q. T., Goryachev, S., Weiss, S., Sordo, M., Murphy, S. N., & Lazarus, R.
- Affiliations
- Brigham and Women's Hospital (US)
- Harvard University (US)
- Corresponding author
- Zeng, Qing T
- Venue
- BMC Medical Informatics and Decision Making
- Published
- 2006-07-26
- Volume
- 6
- Issue
- 1
- Pages
- 30-30
- ISSN
- 1472-6947 (ISSN-L)
- OpenAlex
- W1602694398
- Cited by
- 360
- References
- 26
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 14 · 2023: 12 · 2022: 5 · 2021: 20 · 2020: 18 · 2019: 29 · 2018: 21 · 2017: 22 · 2016: 26 · 2015: 22 · 2014: 27 · 2013: 29 · 2012: 29
- FWCI
- 5.02
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BMC Medical Informatics and Decision Making (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institutes of Health (LM008748)
- U.S. National Library of Medicine (U54LM008748)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Text Analysis Techniques
- Keywords
- natural language processing, clinical text extraction, electronic health records, asthma, discharge summaries, airway disease, extraction accuracy
- MeSH terms
- Asthma, Humans, Natural Language Processing, Patient Discharge, Sensitivity and Specificity, Smoking, Comorbidity, Medical Records Systems, Computerized, Pulmonary Disease, Chronic Obstructive, International Classification of Diseases
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