@inproceedings{yuan-etal-2024-continued,
title = "A Continued Pretrained {LLM} Approach for Automatic Medical Note Generation",
author = "Yuan, Dong and
Rastogi, Eti and
Naik, Gautam and
Rajagopal, Sree Prasanna and
Goyal, Sagar and
Zhao, Fen and
Chintagunta, Bharath and
Ward, Jeffrey",
editor = "Duh, Kevin and
Gomez, Helena and
Bethard, Steven",
booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.naacl-short.47",
doi = "10.18653/v1/2024.naacl-short.47",
pages = "565--571",
abstract = "LLMs are revolutionizing NLP tasks. However, the use of the most advanced LLMs, such as GPT-4, is often prohibitively expensive for most specialized fields. We introduce HEAL, the first continuously trained 13B LLaMA2-based LLM that is purpose-built for medical conversations and measured on automated scribing. Our results demonstrate that HEAL outperforms GPT-4 and PMC-LLaMA in PubMedQA, with an accuracy of 78.4{\%}. It also achieves parity with GPT-4 in generating medical notes. Remarkably, HEAL surpasses GPT-4 and Med-PaLM 2 in identifying more correct medical concepts and exceeds the performance of human scribes and other comparable models in correctness and completeness.",
}
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%0 Conference Proceedings
%T A Continued Pretrained LLM Approach for Automatic Medical Note Generation
%A Yuan, Dong
%A Rastogi, Eti
%A Naik, Gautam
%A Rajagopal, Sree Prasanna
%A Goyal, Sagar
%A Zhao, Fen
%A Chintagunta, Bharath
%A Ward, Jeffrey
%Y Duh, Kevin
%Y Gomez, Helena
%Y Bethard, Steven
%S Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F yuan-etal-2024-continued
%X LLMs are revolutionizing NLP tasks. However, the use of the most advanced LLMs, such as GPT-4, is often prohibitively expensive for most specialized fields. We introduce HEAL, the first continuously trained 13B LLaMA2-based LLM that is purpose-built for medical conversations and measured on automated scribing. Our results demonstrate that HEAL outperforms GPT-4 and PMC-LLaMA in PubMedQA, with an accuracy of 78.4%. It also achieves parity with GPT-4 in generating medical notes. Remarkably, HEAL surpasses GPT-4 and Med-PaLM 2 in identifying more correct medical concepts and exceeds the performance of human scribes and other comparable models in correctness and completeness.
%R 10.18653/v1/2024.naacl-short.47
%U https://aclanthology.org/2024.naacl-short.47
%U https://doi.org/10.18653/v1/2024.naacl-short.47
%P 565-571
Markdown (Informal)
[A Continued Pretrained LLM Approach for Automatic Medical Note Generation](https://aclanthology.org/2024.naacl-short.47) (Yuan et al., NAACL 2024)
ACL
- Dong Yuan, Eti Rastogi, Gautam Naik, Sree Prasanna Rajagopal, Sagar Goyal, Fen Zhao, Bharath Chintagunta, and Jeffrey Ward. 2024. A Continued Pretrained LLM Approach for Automatic Medical Note Generation. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers), pages 565–571, Mexico City, Mexico. Association for Computational Linguistics.