A Continued Pretrained LLM Approach for Automatic Medical Note Generation
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Dong Yuan, Eti Rastogi, Gautam Naik, Sree Prasanna Rajagopal, Sagar Goyal, Fen Zhao, Bharath Chintagunta, Jeffrey Ward. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers). 2024.
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Domain-specific LLMs perform well in clinical text summarization, note generation, and medical question answering.
“Domain-specific LLMs, fine-tuned on medical corpora or using knowledge graph embeddings, perform well in clinical text summarization [10, 11], note generation [12, 13], and medical question answering [14].”
From Enhancing hepatopathy clinical trial efficiency: a secure, large language model-powered pre-screening pipeline (2025) · cites this paper as [13]
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Research works printed but not linked: 5
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- Authors
- Yuan, D., Rastogi, E., Naik, G., Rajagopal, S. P., Goyal, S., Zhao, F., Chintagunta, B., & Ward, J.
- Venue
- Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)
- Published
- 2024
- Pages
- 565-571
- OpenAlex
- W4401042843
- Cited by
- 28
- References
- 0
- Citations by year
- 2026: 5 · 2025: 19 · 2024: 4
- FWCI
- 8.08
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) (publishedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- pre-trained language models, clinical text generation
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