NLP modeling recommendations for restricted data availability in clinical settings
Our study provides evidence-based recommendations for clinical NLP practitioners on selecting modeling paradigms based on data availability. We highlight the importance of considering data availability, task complexity, and institutional maturity when designing and training clinical NLP models. Our findings can inform the development of effective clinical NLP solutions in real-world settings.
Authors
- Jocelyn Dunstan (ORCID: https://orcid.org/0000-0001-6726-7242)
- Felipe Bravo-Márquez (ORCID: https://orcid.org/0000-0002-2153-4306)
- Fabián Villena (ORCID: https://orcid.org/0000-0002-8759-466X)
Institutions
- San Sebastián University (CL)
- Millennium Institute for Integrative Biology (CL)
- University of Chile (CL)
Publication Details
- Journal
- BMC Medical Informatics and Decision Making
- Published
- 2025-03-07
- DOI
- https://doi.org/10.1186/s12911-025-02948-2
- Citations
- 2
- Primary Topic
- Topic Modeling
- Type
- article
- Field-Weighted Citation Impact
- 4.96
Funders
- Agencia Nacional de Investigación y Desarrollo
- Agencia Nacional de Promoción de la Investigación, el Desarrollo Tecnológico y la Innovación