Automatic Summarization of Doctor-Patient Encounter Dialogues Using Large Language Model through Prompt Tuning

• URL: http://arxiv.org/abs/2403.13089v1
• Date: Tue, 19 Mar 2024 18:37:05 GMT
• Title: Automatic Summarization of Doctor-Patient Encounter Dialogues Using Large Language Model through Prompt Tuning
• Authors: Mengxian Lyu, Cheng Peng, Xiaohan Li, Patrick Balian, Jiang Bian, Yonghui Wu,
• Abstract summary: This study presents an approach to summarize doctor-patient dialogues using generative large language models (LLMs) We developed prompt-tuning algorithms to instruct generative LLMs to summarize clinical text.
• Score: 20.9626587328674
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automatic text summarization (ATS) is an emerging technology to assist clinicians in providing continuous and coordinated care. This study presents an approach to summarize doctor-patient dialogues using generative large language models (LLMs). We developed prompt-tuning algorithms to instruct generative LLMs to summarize clinical text. We examined the prompt-tuning strategies, the size of soft prompts, and the few-short learning ability of GatorTronGPT, a generative clinical LLM developed using 277 billion clinical and general English words with up to 20 billion parameters. We compared GatorTronGPT with a previous solution based on fine-tuning of a widely used T5 model, using a clinical benchmark dataset MTS-DIALOG. The experimental results show that the GatorTronGPT- 20B model achieved the best performance on all evaluation metrics. The proposed solution has a low computing cost as the LLM parameters are not updated during prompt-tuning. This study demonstrates the efficiency of generative clinical LLMs for clinical ATS through prompt tuning.

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