通过上下文感知的概念瓶颈模型改进ARDS诊断
Improving ARDS Diagnosis Through Context-Aware Concept Bottleneck Models
- Imperial College London（帝国理工学院伦敦校区）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对临床数据集标签缺失与AI模型可解释性受限的问题，本研究提出利用大型语言模型处理临床笔记生成额外概念，构建上下文感知的概念瓶颈模型，有效提升了ARDS识别性能并降低了信息泄漏风险。
AI中文摘要：
大型公开可用的临床数据集已成为理解疾病异质性和探索治疗个性化的一种新资源。这些数据集来源于最初并非为研究目的而收集的数据，因此往往是不完整的，并且缺乏关键标签。已经开发了许多AI工具来对这些数据集进行回顾性标记，例如通过执行疾病分类；然而，它们往往受到可解释性有限的困扰。先前的工作曾尝试使用概念瓶颈模型来解释预测，这些模型学习可解释的概念并映射到更高层次的临床理念，从而促进人类评估。然而，当这些概念无法充分解释或表征任务时，这些模型往往会遇到性能限制。我们使用急性呼吸窘迫综合征的识别作为一个具有挑战性的测试案例，以证明结合临床笔记中的上下文信息来改进CBM性能的价值。我们的方法利用大型语言模型处理临床笔记并生成额外概念，与现有方法相比实现了10%的性能提升。此外，它还促进了更全面概念的学习，从而降低了信息泄漏的风险和对虚假捷径的依赖，进而改进了对ARDS的表征。
英文摘要：
Large, publicly available clinical datasets have emerged as a novel resource for understanding disease heterogeneity and to explore personalization of therapy. These datasets are derived from data not originally collected for research purposes and, as a result, are often incomplete and lack critical labels. Many AI tools have been developed to retrospectively label these datasets, such as by performing disease classification; however, they often suffer from limited interpretability. Previous work has attempted to explain predictions using Concept Bottleneck Models (CBMs), which learn interpretable concepts that map to higher-level clinical ideas, facilitating human evaluation. However, these models often experience performance limitations when the concepts fail to adequately explain or characterize the task. We use the identification of Acute Respiratory Distress Syndrome (ARDS) as a challenging test case to demonstrate the value of incorporating contextual information from clinical notes to improve CBM performance. Our approach leverages a Large Language Model (LLM) to process clinical notes and generate additional concepts, resulting in a 10% performance gain over existing methods. Additionally, it facilitates the learning of more comprehensive concepts, thereby reducing the risk of information leakage and reliance on spurious shortcuts, thus improving the characterization of ARDS.
