IntelliCare：利用来自大型语言模型的方差控制患者级知识改进医疗保健分析
IntelliCare: Improving Healthcare Analysis with Variance-Controlled Patient-Level Knowledge from Large Language Models
AI总结：
本文提出IntelliCare框架，通过利用LLM生成方差控制的患者级外部知识并结合统计信息与混合校准方法增强EHR模型，在多个临床预测任务上显著提升了性能。
摘要：
虽然开创性的深度学习方法在分析电子健康记录（EHR）数据方面取得了巨大进展，但它们往往难以从有限数据中完全捕捉多样化医疗代码的语义。从大型语言模型（LLMs）整合外部知识为改善医疗保健预测提供了一条有前景的途径。然而，由于歧义问题和不一致性问题，LLM分析可能表现出显著的方差，阻碍了其有效利用。为了应对这些挑战，我们提出了IntelliCare，这是一个新颖的框架，利用LLM提供高质量的患者级外部知识并增强现有的EHR模型。具体而言，IntelliCare识别患者队列，并采用与任务相关的统计信息来增强LLM的理解和生成能力，从而有效缓解歧义问题。此外，它通过一种混合方法精炼LLM衍生的知识，生成多份分析，并使用EHR模型和困惑度度量对其进行校准。在两个大规模EHR数据集上的三个临床预测任务上的实验评估表明，IntelliCare为现有方法带来了显著的性能提升，凸显了其在推进个性化医疗保健预测和决策支持系统方面的潜力。
英文摘要：
While pioneering deep learning methods have made great strides in analyzing electronic health record (EHR) data, they often struggle to fully capture the semantics of diverse medical codes from limited data. The integration of external knowledge from Large Language Models (LLMs) presents a promising avenue for improving healthcare predictions. However, LLM analyses may exhibit significant variance due to ambiguity problems and inconsistency issues, hindering their effective utilization. To address these challenges, we propose IntelliCare, a novel framework that leverages LLMs to provide high-quality patient-level external knowledge and enhance existing EHR models. Concretely, IntelliCare identifies patient cohorts and employs task-relevant statistical information to augment LLM understanding and generation, effectively mitigating the ambiguity problem. Additionally, it refines LLM-derived knowledge through a hybrid approach, generating multiple analyses and calibrating them using both the EHR model and perplexity measures. Experimental evaluations on three clinical prediction tasks across two large-scale EHR datasets demonstrate that IntelliCare delivers significant performance improvements to existing methods, highlighting its potential in advancing personalized healthcare predictions and decision support systems.
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。
