تقرير
A Continued Pretrained LLM Approach for Automatic Medical Note Generation
| العنوان: | A Continued Pretrained LLM Approach for Automatic Medical Note Generation |
|---|---|
| المؤلفون: | Yuan, Dong, Rastogi, Eti, Naik, Gautam, Rajagopal, Sree Prasanna, Goyal, Sagar, Zhao, Fen, Chintagunta, Bharath, Ward, Jeff |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computation and Language, Computer Science - Artificial Intelligence |
| الوصف: | 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. Comment: Accepted to NAACL 2024 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2403.09057 |
| رقم الأكسشن: | edsarx.2403.09057 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |