通过对2型糖尿病风险预测模型的背景化事后解释为临床评估提供信息
Informing clinical assessment by contextualizing post-hoc explanations of risk prediction models in type-2 diabetes
摘要：
医学专家可以更信任地使用人工智能（AI）系统，如果这些系统得到上下文解释的支持，让从业者将系统推断与其使用上下文联系起来。然而，它们在提高模型使用和理解方面的重要性尚未得到广泛研究。因此，我们考虑合并症风险预测场景，并关注患者临床状态的背景、关于并发症风险的AI预测以及支持预测的算法解释。我们将探讨如何从医疗指南中提取这些维度的相关信息，以回答临床医生的典型问题。我们确定这是一个问答（QA）任务，并采用几个国家的最先进的法学硕士风险预测模型推断的背景下，并评估其可接受性。最后，我们通过构建一个端到端的人工智能管道来研究上下文解释的好处，包括数据集合，人工智能风险建模，事后模型解释，并建立一个可视化仪表板原型，以呈现来自不同上下文维度和数据源的综合见解，同时预测和识别慢性肾脏病风险的驱动因素-一种常见的2型糖尿病合并症。所有这些步骤都是在医疗专家的参与下进行的，包括由一个专家医疗小组对仪表板结果进行最终评估。我们表明，LLM，特别是BERT和SciBERT，可以很容易地部署提取一些相关的解释，以支持临床使用。为了理解上下文解释的附加值，专家小组评估了这些关于相关临床环境中可操作的见解。总的来说，我们的论文是第一个端到端的分析，确定了在现实世界的临床用例中上下文解释的可行性和好处。
英文摘要：
Medical experts may use Artificial Intelligence (AI) systems with greater trust if these are supported by contextual explanations that let the practitioner connect system inferences to their context of use. However, their importance in improving model usage and understanding has not been extensively studied. Hence, we consider a comorbidity risk prediction scenario and focus on contexts regarding the patients clinical state, AI predictions about their risk of complications, and algorithmic explanations supporting the predictions. We explore how relevant information for such dimensions can be extracted from Medical guidelines to answer typical questions from clinical practitioners. We identify this as a question answering (QA) task and employ several state-of-the-art LLMs to present contexts around risk prediction model inferences and evaluate their acceptability. Finally, we study the benefits of contextual explanations by building an end-to-end AI pipeline including data cohorting, AI risk modeling, post-hoc model explanations, and prototyped a visual dashboard to present the combined insights from different context dimensions and data sources, while predicting and identifying the drivers of risk of Chronic Kidney Disease - a common type-2 diabetes comorbidity. All of these steps were performed in engagement with medical experts, including a final evaluation of the dashboard results by an expert medical panel. We show that LLMs, in particular BERT and SciBERT, can be readily deployed to extract some relevant explanations to support clinical usage. To understand the value-add of the contextual explanations, the expert panel evaluated these regarding actionable insights in the relevant clinical setting. Overall, our paper is one of the first end-to-end analyses identifying the feasibility and benefits of contextual explanations in a real-world clinical use case.
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。
