ReviewIEEE/ACM transactions on computational biology and bioinformatics
Natural Language Processing for EHR-Based Computational Phenotyping.
Review in IEEE/ACM transactions on computational biology and bioinformatics. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 91 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
91 citing papers in PubMed.
- Maternal Vulnerability Indicators Derived from Free-Text Clinical Documentation in Adolescent Pregnancy.Medicina (Kaunas, Lithuania) · 2026Article
- Introducing the TOP framework: a novel phenotyping solution for collaborative phenotype algorithm development and application.Journal of biomedical semantics · 2026Article
- Unsupervised characterization of 100,272 EHR patients identifies high-risk groups and comorbidities linked to premature aging.NPJ digital medicine · 2026Article
- Retrospective Cohort Study: Extracting Coexisting Background Breast-Lesion Features from Stage I-III Invasive Breast Cancer.Cancers · 2026Article
- Artificial Intelligence in Heart Failure with Preserved Ejection Fraction.Diagnostics (Basel, Switzerland) · 2026Review
- Scalable Identification of Clinically Relevant Chronic Obstructive Pulmonary Disease Documents in Large-Scale Electronic Health Record Datasets With a Lightweight Natural Language Processing Model: Retrospective Cohort Study.JMIR medical informatics · 2026Article
- PrecLLM: A Privacy-Preserving Framework for Efficient Clinical Annotation Extraction from Unstructured EHRs using Small-Scale LLMs.Research square · 2026Article
- ChatGPT in precision medicine.APL bioengineering · 2026Review
- Assessing genetic counseling efficiency with natural language processing.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Automating clinical phenotyping using natural language processing.Communications medicine · 2026Article
- An interpretable computational phenotyping pipeline for adult autism using routine electronic health records.Frontiers in psychiatry · 2026Article
- Identification and validation of respiratory virus immunization using natural language processing.Frontiers in digital health · 2026Article
- A Multi-Model LLM Consensus Framework to Identify EHR-Predictable Eligibility Criteria in NSCLC Immunotherapy Trials.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026Article
- Large language model derived regular expressions for sleep phenotyping from electronic health record: a feasibility study.Sleep advances : a journal of the Sleep Research Society · 2026Article
- Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models: Quantitative Study Using Large Language Models.Journal of medical Internet research · 2025Article
- PERADIGM: Phenotype embedding similarity-based rare disease gene mapping.PLoS genetics · 2025Article
- Natural Language Processing andJMIR medical informatics · 2025Article
- Improving automated deep phenotyping through large language models using retrieval-augmented generation.Genome medicine · 2025Article
- Leveraging undecided cases in chart-reviewed phenotypes to enhance EHR-based association studies.Journal of biomedical informatics · 2025Article
- Utilization of Computable Phenotypes in Electronic Health Record Research: A Review and Case Study in Atopic Dermatitis.The Journal of investigative dermatology · 2025Review
31 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
This article reviews recent advances in applying natural language processing (NLP) to Electronic Health Records (EHRs) for computational phenotyping. NLP-based computational phenotyping has numerous applications including diagnosis categorization, novel phenotype discovery, clinical trial screening, pharmacogenomics, drug-drug interaction (DDI), and adverse drug event (ADE) detection, as well as genome-wide and phenome-wide association studies. Significant progress has been made in algorithm development and resource construction for computational phenotyping. Among the surveyed methods, well-designed keyword search and rule-based systems often achieve good performance. However, the construction of keyword and rule lists requires significant manual effort, which is difficult to scale. Supervised machine learning models have been favored because they are capable of acquiring both classification patterns and structures from data. Recently, deep learning and unsupervised learning have received growing attention, with the former favored for its performance and the latter for its ability to find novel phenotypes. Integrating heterogeneous data sources have become increasingly important and have shown promise in improving model performance. Often, better performance is achieved by combining multiple modalities of information. Despite these many advances, challenges and opportunities remain for NLP-based computational phenotyping, including better model interpretability and generalizability, and proper characterization of feature relations in clinical narratives.
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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.