生成患者-医生对话的医学准确摘要：使用大型语言模型的多阶段方法
Generating medically-accurate summaries of patient-provider dialogue: A multi-stage approach using large language models
AI总结：
本文提出一种多阶段方法，利用GPT-3将医学对话摘要分解为实体识别等子任务，生成临床准确摘要，优于零样本基线。
摘要：
医疗提供者对患者就诊的摘要具有多种关键用途，包括临床决策、促进提供者之间的交接，以及作为患者的参考。有效的摘要需要连贯且准确捕捉对话中所有医学相关信息，尽管患者生成的语言具有复杂性。即使就诊摘要中的轻微不准确（例如，在患者有发热时总结为“患者没有发热”）也可能对患者的护理结果产生不利影响。\n本文通过将任务离散化为几个依次构建的较小对话理解子任务，来解决医学对话摘要问题。首先，我们在对话中识别医学实体及其肯定性表述，作为构建块。我们研究通过根据相关患者信息进行条件化，动态构建少样本提示的方法，并使用GPT-3作为我们实验的骨干模型。我们还开发了基于GPT的摘要指标，以定量衡量与参考摘要相比的性能。我们的人工评估研究和医学正确性指标均表明，使用这种方法生成的摘要在临床上准确，并优于在零样本、单提示设置中总结对话的基线方法。
英文摘要：
A medical provider's summary of a patient visit serves several critical purposes, including clinical decision-making, facilitating hand-offs between providers, and as a reference for the patient. An effective summary is required to be coherent and accurately capture all the medically relevant information in the dialogue, despite the complexity of patient-generated language. Even minor inaccuracies in visit summaries (for example, summarizing "patient does not have a fever" when a fever is present) can be detrimental to the outcome of care for the patient. This paper tackles the problem of medical conversation summarization by discretizing the task into several smaller dialogue-understanding tasks that are sequentially built upon. First, we identify medical entities and their affirmations within the conversation to serve as building blocks. We study dynamically constructing few-shot prompts for tasks by conditioning on relevant patient information and use GPT-3 as the backbone for our experiments. We also develop GPT-derived summarization metrics to measure performance against reference summaries quantitatively. Both our human evaluation study and metrics for medical correctness show that summaries generated using this approach are clinically accurate and outperform the baseline approach of summarizing the dialog in a zero-shot, single-prompt setting.
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。
