ArticlePatterns (New York, N.Y.)2021
Structuring clinical text with AI: Old versus new natural language processing techniques evaluated on eight common cardiovascular diseases.
Article in Patterns (New York, N.Y.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Artificial Intelligence in Cardiovascular Medicine: A Giant Step in Personalized Medicine?Journal of personalized medicine · 2026Review
- Leveraging a large language model to support expansion of surveillance activities to include cardiovascular implantable device infections in a large, integrated national healthcare system.Infection control and hospital epidemiology · 2026Article
- AI in the Hot Seat: Head-to-Head Comparison of Large Language Models and Cardiologists in Emergency Scenarios.Medical sciences (Basel, Switzerland) · 2026Article
- Utilizing large language models and natural language processing to classify ischemia status from cardiac stress tests in a large multicenter healthcare system.BMC research notes · 2025Article
- Identifying Diabetes Related-Complications in a Real-World Free-Text Electronic Medical Records in Hebrew Using Natural Language Processing Techniques.Journal of diabetes science and technology · 2025Article
- Flexible imputation toolkit for electronic health records.Scientific reports · 2025Article
- Reliability-enhanced data cleaning in biomedical machine learning using inductive conformal prediction.PLoS computational biology · 2025Article
- Sample Size Considerations for Fine-Tuning Large Language Models for Named Entity Recognition Tasks: Methodological Study.JMIR AI · 2024Article
- Artificial intelligence: revolutionizing cardiology with large language models.European heart journal · 2024Article
- Prospects for AI clinical summarization to reduce the burden of patient chart review.Frontiers in digital health · 2024Review
- An interpretable machine learning framework for opioid overdose surveillance from emergency medical services records.PloS one · 2024Article
- Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review.JMIR medical informatics · 2023Review
- Ontologizing health systems data at scale: making translational discovery a reality.NPJ digital medicine · 2023Article
- Heart disease risk factors detection from electronic health records using advanced NLP and deep learning techniques.Scientific reports · 2023Article
- Classification of neurologic outcomes from medical notes using natural language processing.Expert systems with applications · 2023Article
- Hybrid deep learning models with multi-classification investor sentiment to forecast the prices of China's leading stocks.PloS one · 2023Article
- Comparative analysis of classification techniques for topic-based biomedical literature categorisation.Frontiers in genetics · 2023Article
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Authors and funding
4 authors.
Funding
Abstract
Free-text clinical notes in electronic health records are more difficult for data mining while the structured diagnostic codes can be missing or erroneous. To improve the quality of diagnostic codes, this work extracts diagnostic codes from free-text notes: five old and new word vectorization methods were used to vectorize Stanford progress notes and predict eight ICD-10 codes of common cardiovascular diseases with logistic regression. The models showed good performance, with TF-IDF as the best vectorization model showing the highest AUROC (0.9499-0.9915) and AUPRC (0.2956-0.8072). The models also showed transferability when tested on MIMIC-III data with AUROC from 0.7952 to 0.9790 and AUPRC from 0.2353 to 0.8084. Model interpretability was shown by the important words with clinical meanings matching each disease. This study shows the feasibility of accurately extracting structured diagnostic codes, imputing missing codes, and correcting erroneous codes from free-text clinical notes for information retrieval and downstream machine-learning applications.
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