基于知识条件化LLM的自动化临床数据提取
Automated Clinical Data Extraction with Knowledge Conditioned LLMs
AI总结：
本文提出一种基于上下文学习（ICL）的知识对齐框架，通过检索器和评分器结合内外部知识，解决LLM在临床报告提取中的幻觉问题，将关键字段F1分数平均提升12.9%。
摘要：
从临床和医学影像报告中提取肺部病灶信息，对于肺部疾病的研究和临床护理至关重要。大型语言模型（LLM）能够有效解释报告中的非结构化文本，但由于缺乏领域特定知识，它们经常产生幻觉，导致准确性降低，并给临床环境中的应用带来挑战。为了解决这一问题，我们提出了一种新颖的框架，通过上下文学习（ICL）将生成的内部知识与外部知识对齐。我们的框架采用检索器来识别内部或外部知识的相关单元，并使用评分器来评估检索到的内部知识规则的真实性和有用性，以对齐和更新知识库。使用专家策划的测试数据集进行的实验表明，与现有的ICL方法相比，这种ICL方法可以将关键字段（病灶大小、边缘和实性）的F1分数平均提高12.9%。
英文摘要：
The extraction of lung lesion information from clinical and medical imaging reports is crucial for research on and clinical care of lung-related diseases. Large language models (LLMs) can be effective at interpreting unstructured text in reports, but they often hallucinate due to a lack of domain-specific knowledge, leading to reduced accuracy and posing challenges for use in clinical settings. To address this, we propose a novel framework that aligns generated internal knowledge with external knowledge through in-context learning (ICL). Our framework employs a retriever to identify relevant units of internal or external knowledge and a grader to evaluate the truthfulness and helpfulness of the retrieved internal-knowledge rules, to align and update the knowledge bases. Experiments with expert-curated test datasets demonstrate that this ICL approach can increase the F1 score for key fields (lesion size, margin and solidity) by an average of 12.9% over existing ICL methods.
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。
