ArticlePloS one2018
Comparing deep learning and concept extraction based methods for patient phenotyping from clinical narratives.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 78 papers.
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Who cites it
78 citing papers in PubMed.
- Explainable AI for mental health emergency returns: integrating large language models with predictive modeling.JAMIA open · 2026Article
- Note-Level Phenotyping of Multiple-Sclerosis Notes by a Large Language Model Achieves near Human-Level Agreement.Journal of clinical medicine · 2026Article
- Comorbidity Classification from Clinical Free-Text using Large Language Models: Application to Sleep Disorder Patients.Journal of medical systems · 2026Article
- Generalizing machine learning models from clinical free text.Scientific reports · 2025Article
- Role of artificial intelligence in revolutionizing drug discovery.Fundamental research · 2025Review
- The Association between All-Cause Mortality and Obstructive Sleep Apnea in Adults: A U-Shaped Curve.Annals of the American Thoracic Society · 2025Article
- Utility of word embeddings from large language models in medical diagnosis.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Analyzing patient perspectives with large language models: a cross-sectional study of sentiment and thematic classification on exception from informed consent.Scientific reports · 2025Article
- A hybrid framework with large language models for rare disease phenotyping.BMC medical informatics and decision making · 2024Article
- Article
- A taxonomy for advancing systematic error analysis in multi-site electronic health record-based clinical concept extraction.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Retrieval-Based Diagnostic Decision Support: Mixed Methods Study.JMIR medical informatics · 2024Article
- Artificial intelligence to unlock real-world evidence in clinical oncology: A primer on recent advances.Cancer medicine · 2024Review
- Identifying social determinants of health from clinical narratives: A study of performance, documentation ratio, and potential bias.Journal of biomedical informatics · 2024Article
- Use of Artificial Intelligence in Improving Outcomes in Heart Disease: A Scientific Statement From the American Heart Association.Circulation · 2024Review
- Semi-supervised ROC analysis for reliable and streamlined evaluation of phenotyping algorithms.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Comparison of Prompt Engineering and Fine-Tuning Strategies in Large Language Models in the Classification of Clinical Notes.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2024Article
- A Large Language Model Outperforms Other Computational Approaches to the High-Throughput Phenotyping of Physician Notes.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Automated HIV Case Identification from the MIMIC-IV Database.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2024Article
- Selective prediction for extracting unstructured clinical data.Journal of the American Medical Informatics Association : JAMIA · 2023Article
18 more citing papers are in PubMed but not listed here.
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Authors and funding
11 authors.
Funding
Abstract
In secondary analysis of electronic health records, a crucial task consists in correctly identifying the patient cohort under investigation. In many cases, the most valuable and relevant information for an accurate classification of medical conditions exist only in clinical narratives. Therefore, it is necessary to use natural language processing (NLP) techniques to extract and evaluate these narratives. The most commonly used approach to this problem relies on extracting a number of clinician-defined medical concepts from text and using machine learning techniques to identify whether a particular patient has a certain condition. However, recent advances in deep learning and NLP enable models to learn a rich representation of (medical) language. Convolutional neural networks (CNN) for text classification can augment the existing techniques by leveraging the representation of language to learn which phrases in a text are relevant for a given medical condition. In this work, we compare concept extraction based methods with CNNs and other commonly used models in NLP in ten phenotyping tasks using 1,610 discharge summaries from the MIMIC-III database. We show that CNNs outperform concept extraction based methods in almost all of the tasks, with an improvement in F1-score of up to 26 and up to 7 percentage points in area under the ROC curve (AUC). We additionally assess the interpretability of both approaches by presenting and evaluating methods that calculate and extract the most salient phrases for a prediction. The results indicate that CNNs are a valid alternative to existing approaches in patient phenotyping and cohort identification, and should be further investigated. Moreover, the deep learning approach presented in this paper can be used to assist clinicians during chart review or support the extraction of billing codes from text by identifying and highlighting relevant phrases for various medical conditions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.