使用检索增强生成的电子健康记录问答式摘要
Question-Answering Based Summarization of Electronic Health Records using Retrieval Augmented Generation
AI总结：
本文提出一种结合语义搜索、检索增强生成（RAG）和问答的方法，利用最新LLMs对电子健康记录进行摘要，以解决标注数据不足和注意力机制二次复杂度导致的性能问题，同时避免幻觉并确保内容多样性。
摘要：
电子健康记录（EHRs）的摘要可以大幅减少患者和医务人员的“屏幕时间”。近年来，EHRs的摘要采用了使用最先进神经模型的机器学习流水线。然而，这些模型产生的结果不尽如人意，原因在于难以获得足够的标注数据用于训练。此外，在摘要中需要考虑EHR的全部内容，这导致了性能不佳，因为现代大型语言模型（LLMs）中的注意力机制在输入大小方面增加了二次复杂度。我们在此提出一种方法，通过结合语义搜索、检索增强生成（RAG）和使用最新LLMs的问答来缓解这些缺陷。在我们的方法中，摘要是对主题专家（SMEs）认为重要的特定问题的答案提取。我们的方法非常高效；几乎不需要训练；不会受到LLMs的“幻觉”问题影响；并且它确保了多样性，因为摘要不会有重复内容，而是对特定问题的多样化答案。
英文摘要：
Summarization of electronic health records (EHRs) can substantially minimize 'screen time' for both patients as well as medical personnel. In recent years summarization of EHRs have employed machine learning pipelines using state of the art neural models. However, these models have produced less than adequate results that are attributed to the difficulty of obtaining sufficient annotated data for training. Moreover, the requirement to consider the entire content of an EHR in summarization has resulted in poor performance due to the fact that attention mechanisms in modern large language models (LLMs) adds a quadratic complexity in terms of the size of the input. We propose here a method that mitigates these shortcomings by combining semantic search, retrieval augmented generation (RAG) and question-answering using the latest LLMs. In our approach summarization is the extraction of answers to specific questions that are deemed important by subject-matter experts (SMEs). Our approach is quite efficient; requires minimal to no training; does not suffer from the 'hallucination' problem of LLMs; and it ensures diversity, since the summary will not have repeated content but diverse answers to specific questions.
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。
