ArticleJCO clinical cancer informatics2021
Automated Identification of Patients With Immune-Related Adverse Events From Clinical Notes Using Word Embedding and Machine Learning.
Article in JCO clinical cancer informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Cancer and treatment specific incidence rates of immune-related adverse events induced by immune checkpoint inhibitors: a systematic review.British journal of cancer · 2025Pooled it
- Laboratory Medicine in Immune Checkpoint Inhibitor-Induced Autoimmune Disorders: Baseline Assessment, Diagnostic Algorithms, Monitoring, and Predictive Biomarkers.Annals of laboratory medicine · 2026Review
- Hierarchical prompting with reasoning large language models for immune checkpoint inhibitor-associated myocarditis evidence extraction pilot study.European heart journal. Digital health · 2026Article
- Population-Scale Precision Safety in Oncology Reveals Clinical and Genetic Determinants of Systemic Therapy Toxicity.medRxiv : the preprint server for health sciences · 2026Article
- A Clinically Grounded Rule-Based Phenotyping Framework for Identifying Immune-Related Adverse Events in Structured Electronic Health Record Data: A Retrospective EHR Study.Research square · 2026Article
- irAE-GPT: leveraging large language models to identify immune-related adverse events in electronic health records and clinical trial datasets.EBioMedicine · 2026Article
- An agentic AI system enhances clinical detection of immunotherapy toxicities: a multi-phase validation study.medRxiv : the preprint server for health sciences · 2026Article
- Large Language Models for Drug-Related Adverse Events in Oncology Pharmacy: Detection, Grading, and Actioning.Pharmacy (Basel, Switzerland) · 2025Review
- Large language models accurately identify immunosuppression in intensive care unit patients.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Immunotherapy-Associated Cardiotoxicity: Current Insights and Future Directions for Precision Cardio-Oncology.Cancers · 2025Review
- Elucidating Celecoxib's Preventive Effect in Capecitabine-Induced Hand-Foot Syndrome Using Medical Natural Language Processing.JCO clinical cancer informatics · 2025Article
- Validation of Immune-Related Adverse Event (irAE) Case Definitions in a Real-World Lung Cancer Population.Pharmacoepidemiology and drug safety · 2025Article
- Artificial intelligence-enabled safety monitoring in Alzheimer's disease clinical trials.The journal of prevention of Alzheimer's disease · 2025Article
- Therapeutic Vaccination in Lung Cancer: Past Attempts, Current Approaches and Future Promises.Journal of respiratory biology and translational medicine · 2025Article
- Enhancing Precision in Detecting Severe Immune-Related Adverse Events: Comparative Analysis of Large Language Models and International Classification of Disease Codes in Patient Records.Journal of clinical oncology : official journal of the American Society of Clinical Oncology · 2024Article
- Promise and Perils of Large Language Models for Cancer Survivorship and Supportive Care.Journal of clinical oncology : official journal of the American Society of Clinical Oncology · 2024Article
- Navigating the Complexities of Artificial Intelligence-Enabled Real-World Data Collection for Oncology Pharmacovigilance.JCO clinical cancer informatics · 2024Article
- Identification and Characterization of Immune Checkpoint Inhibitor-Induced Toxicities From Electronic Health Records Using Natural Language Processing.JCO clinical cancer informatics · 2024Article
- Detection of Patient-Level Immunotherapy-Related Adverse Events (irAEs) from Clinical Narratives of Electronic Health Records: A High-Sensitivity Artificial Intelligence Model.Pragmatic and observational research · 2024Article
- Adverse drug event detection using natural language processing: A scoping review of supervised learning methods.PloS one · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
purposeAlthough immune checkpoint inhibitors (ICIs) have substantially improved survival in patients with advanced malignancies, they are associated with a unique spectrum of side effects termed immune-related adverse events (irAEs). To ensure treatment safety, research efforts are needed to comprehensively detect and understand irAEs. Retrospective analysis of data from electronic health records can provide knowledge to characterize these toxicities. However, such information is not captured in a structured format within the electronic health record and requires manual chart review. MATERIALS AND
methodsIn this work, we propose a natural language processing pipeline that can automatically annotate clinical notes and determine whether there is evidence that a patient developed an irAE. Seven hundred eighty-one cases were manually reviewed by clinicians and annotated for irAEs at the patient level. A dictionary of irAEs keywords was used to perform text reduction on clinical notes belonging to each patient; only sentences with relevant expressions were kept. Word embeddings were then used to generate vector representations over the reduced text, which served as input for the machine learning classifiers. The output of the models was presence or absence of any irAEs. Additional models were built to classify skin-related toxicities, endocrine toxicities, and colitis.
resultsThe model for any irAE achieved an average F1-score = 0.75 and area under the receiver operating characteristic curve = 0.85. This outperformed a basic keyword filtering approach. Although the classifier of any irAEs achieved good accuracy, individual irAE classification still has room for improvement.
conclusionWe demonstrate that patient-level annotations combined with a machine learning approach using keywords filtering and word embeddings can achieve promising accuracy in classifying irAEs in clinical notes. This model may facilitate annotation and analysis of large irAEs data sets.
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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.