基於本地大型語言模型的門診對話臨床紀錄摘要

Abstract

大型語言模型近年來被視為簡化臨床工作流程的有力工具。然而，在高度敏感的醫療領域中應用模型面臨諸多挑戰，例如隱私保護，以及缺乏公開且高品質的臨床對話資料集。本研究聚焦於使用開源大型語言模型，在完全本地環境下，從實際的門診對話中產生臨床紀錄，以確保病患隱私不外洩。我們設計了一套完整的資料前處理流程，包含對實際醫療對話的摘要與翻譯，並重新標註對應的臨床紀錄內容。本文探討三種臨床紀錄生成方式：單階段端到端生成、兩階段檢索增強生成、以及單階段生成搭配合成對話擴充。我們的實驗顯示監督式微調在效能上表現優異，且小型模型在準確檢索關鍵證據方面亦展現潛力。儘管大型語言模型可在一定程度上協助摘要臨床紀錄，但要維持完全在地部署兼顧效能仍是一大挑戰。本論文突顯了當前大型語言模型應用於醫療資料的潛力與限制，特別是在隱私要求高、需本地部署的場景下。

Large language models (LLMs) have emerged as a promising tool to streamline clinical workflows. However, the application of LLMs in the highly sensitive domain of healthcare faces major challenges, such as strict privacy regulations and the scarcity of publicly available, high-quality clinical dialogue datasets. This work focuses on clinical note generation from real-world outpatient conversations using open-source LLMs in a fully local environment to preserve patient privacy. We developed a comprehensive data preprocessing pipeline involving summarization and translation of real-life medical dialogues, along with meticulous re-annotation of the corresponding clinical notes. Three approaches to note generation are explored: One-stage End-to-end Generation, Two-stage Retrieval-Augmented Generation and One-stage Generation with Synthetic Dialogue Augmentation. Our experiments demonstrated the effectiveness of supervised fine-tuning methods and the the potential of smaller models in accurately retrieving evidence. While LLM applications can assist in summarizing clinical notes to a certain extent, maintaining fully local models for privacy remains a significant challenge. This work highlights both the potential and the limitations of current LLM-based approaches in this specialized domain, particularly under local deployment constraints.