ArticleEuropean heart journal. Digital health2024
Using natural language processing for automated classification of disease and to identify misclassified ICD codes in cardiac disease.
Article in European heart journal. Digital health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence in Cardiovascular Medicine: Focus on Hypertension.Hypertension (Dallas, Tex. : 1979) · 2026Pooled it
- Large language models for pneumonia detection in radiology reports via text analysis.PLOS digital health · 2026Article
- Automatic identification of diagnosis from hospital discharge letters via weakly supervised Natural Language Processing.Scientific reports · 2026Article
- Characterization and comparison of structured and unstructured electronic health record data mapped to MedDRA for post-marketing surveillance.JAMIA open · 2026Article
- Comparing three natural language processing methods for the automatic identification of epilepsy patients from French clinical notes.Epilepsia · 2026Article
- The incremental value of unstructured data via natural language processing in machine learning-based COVID-19 mortality prediction: a comparative study.BMC medical informatics and decision making · 2025Article
- Racial and ethnic disparities in aortic stenosis within a universal healthcare system characterized by natural language processing for targeted intervention.European heart journal. Digital health · 2025Article
- Clinical and research applications of natural language processing for heart failure.Heart failure reviews · 2025Review
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Authors and funding
12 authors.
Funding
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Abstract
Aims: ICD codes are used for classification of hospitalizations. The codes are used for administrative, financial, and research purposes. It is known, however, that errors occur. Natural language processing (NLP) offers promising solutions for optimizing the process. To investigate methods for automatic classification of disease in unstructured medical records using NLP and to compare these to conventional ICD coding. Methods and results: Two datasets were used: the open-source Medical Information Mart for Intensive Care (MIMIC)-III dataset ( Conclusion: A newly developed NLP algorithm attained a high accuracy for classifying disease in medical records. XGBoost outperformed the deep learning technique BioBERT. NLP algorithms could be used to identify ICD-coding errors and optimize and support the ICD-coding process.
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