ArticlePragmatic and observational research2024
Detection of Patient-Level Immunotherapy-Related Adverse Events (irAEs) from Clinical Narratives of Electronic Health Records: A High-Sensitivity Artificial Intelligence Model.
Article in Pragmatic and observational research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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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
7 citing papers in PubMed.
- Article
- An agentic AI system enhances clinical detection of immunotherapy toxicities: a multi-phase validation study.medRxiv : the preprint server for health sciences · 2026Article
- Artificial Intelligence for Opioid Safety Surveillance from Clinical Text: A Clinically Focused Review.Journal of clinical medicine · 2026Review
- Diagnostic classification of children and adolescents with high-functioning autism spectrum disorder based on brain functional network characteristics of the dense individualized and common connectivity-based cortical landmark model.Frontiers in psychiatry · 2026Article
- Large Language Models for Drug-Related Adverse Events in Oncology Pharmacy: Detection, Grading, and Actioning.Pharmacy (Basel, Switzerland) · 2025Review
- Large Language Models for Adverse Drug Events: A Clinical Perspective.Journal of clinical medicine · 2025Review
- Critical Challenges in Cancer Immunotherapy and Cardiac Health.Current cardiology reviews · 2025Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: We developed an artificial intelligence (AI) model to detect immunotherapy -related adverse events (irAEs) from clinical narratives of electronic health records (EHRs) at the patient level. Patients and Methods: Training data, used for internal validation of the AI model, comprised 1230 clinical notes from 30 patients at The Ohio State University James Cancer Hospital-20 patients who experienced irAEs and ten who did not. 3256 clinical notes of 50 patients were utilized for external validation of the AI model. Results: Use of a leave-one-out cross-validation technique for internal validation among those 30 patients yielded accurate identification of 19 of 20 with irAEs (positive patients; 95% sensitivity) and correct dissociation of eight of ten without (negative patients; 80% specificity). External validation on 3256 clinical notes of 50 patients yielded high sensitivity (95%) but moderate specificity (64%). If we improve the model's specificity to 100%, it could eliminate the need to manually review 2500 of those 3256 clinical notes (77%). Conclusion: Combined use of this AI model with the manual review of clinical notes will improve both sensitivity and specificity in the detection of irAEs, decreasing workload and costs and facilitating the development of improved immunotherapies.
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Registered trials
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