Exploring Named Entity Recognition Potential and the Value of Tailored Natural Language Processing Pipelines for Radiology, Pathology, and Progress Notes in Clinical Decision Support: Quantitative Study
Further research should explore the analysis of clinical notes beyond the scope of our study, including discharge summaries and psychiatric evaluation notes. Recognizing the unique linguistic characteristics of different note types underscores the importance of developing specialized NER models or natural language processing pipeline setups tailored to each type. By doing so, we can enhance their performance across a more diverse range of clinical scenarios.
Authors
- David Talby (ORCID: https://orcid.org/0000-0003-2782-5478)
- Arash Kia (ORCID: https://orcid.org/0000-0002-4699-7892)
- Veysel Kocaman (ORCID: https://orcid.org/0000-0002-0065-6478)
- Prem Timsina (ORCID: https://orcid.org/0000-0002-6047-887X)
- Ganesh Raut (ORCID: https://orcid.org/0009-0006-7914-7065)
- Fu-Yuan Cheng (ORCID: https://orcid.org/0000-0003-2273-6089)
- Julio Bonis (ORCID: https://orcid.org/0000-0002-0510-7160)
Institutions
- Mount Sinai Health System (US)
- Mount Sinai Hospital (US)
- John Snow (United States) (US)
- Icahn School of Medicine at Mount Sinai (US)
Publication Details
- Journal
- JMIR AI
- Published
- 2025-09-05
- DOI
- https://doi.org/10.2196/59251
- Primary Topic
- Topic Modeling
- Type
- article
- Field-Weighted Citation Impact
- 0.00