从医患对话生成个性化临床记录
Personalized Clinical Note Generation from Doctor-Patient Conversations
AI总结：
本文提出一种个性化临床记录生成技术，通过建模医生对话风格与记录偏好，并在少量配对数据下实现新医生注册而无需重训模型，显著提升各记录部分的 ROUGE-2 分数。
摘要：
在这项工作中，我们提出了一种新技术，用于提高医生草稿临床记录的质量。该技术专注于对隐式医生对话风格和记录偏好进行建模的能力。我们还引入了一种新技术，用于在针对某位医生仅有少量与对话配对的临床记录可用时，为该医生进行注册，而无需重新训练模型来支持他们。我们表明，我们的技术优于基线模型，将现病史部分的 ROUGE-2 分数提高了 13.8%，将体格检查部分提高了 88.6%，将评估与计划部分提高了 50.8%。
英文摘要：
In this work, we present a novel technique to improve the quality of draft clinical notes for physicians. This technique is concentrated on the ability to model implicit physician conversation styles and note preferences. We also introduce a novel technique for the enrollment of new physicians when a limited number of clinical notes paired with conversations are available for that physician, without the need to re-train a model to support them. We show that our technique outperforms the baseline model by improving the ROUGE-2 score of the History of Present Illness section by 13.8%, the Physical Examination section by 88.6%, and the Assessment & Plan section by 50.8%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
