Detection of Patient-Level Immunotherapy-Related Adverse Events (irAEs) from Clinical Narratives of Electronic Health Records: A High-Sensitivity Artificial Intelligence Model
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.
Authors
- Margaret E. Gatti‐Mays (ORCID: https://orcid.org/0000-0001-8914-2897)
- Md Muntasir Zitu (ORCID: https://orcid.org/0000-0001-9103-1467)
- Aditi Shendre (ORCID: https://orcid.org/0000-0003-4123-7613)
- Mohamed I. Elsaid (ORCID: https://orcid.org/0000-0001-7963-2438)
- Lang Li (ORCID: https://orcid.org/0000-0002-0746-1809)
- Shijun Zhang (ORCID: https://orcid.org/0000-0003-0668-1014)
- Kai Johnson
Institutions
- The Ohio State University (US)
Publication Details
- Journal
- Pragmatic and Observational Research
- Published
- 2024-12-01
- DOI
- https://doi.org/10.2147/por.s468253
- Citations
- 6
- Primary Topic
- Pharmacovigilance and Adverse Drug Reactions
- Type
- article
- Field-Weighted Citation Impact
- 2.10