让心血管风险评分变透明，帮医生看清如何降低患者风险。
Enhancing Framingham Cardiovascular Risk Score Transparency through Logic-Based XAI
- 用逻辑推理方法找出决定风险等级的关键身体指标。
- 在2.2万种组合中验证，准确识别出可干预的风险因素。
- 生成可操作建议，适合临床医生和资源有限地区使用。
心血管疾病（CVD）是全球主要健康挑战之一，每年导致超过1900万例死亡。为应对这一问题，多种用于预测CVD风险并支持临床决策的工具已被开发，其中弗雷明汉风险评分（FRS）是应用最广泛且被普遍推荐的工具。然而，该评分无法解释患者为何被划入特定风险等级，也无法说明如何降低风险。由于缺乏透明性，本文提出一种基于一阶逻辑与可解释人工智能（XAI）的逻辑解释器。该解释器能够识别出使某患者获得特定风险分类的最小属性集，并生成可操作的干预场景，指出哪些可控变量若改变可降低风险等级。我们对FRS所有可能输入组合（超过22,000种样本）进行了测试，成功识别出关键风险因素并为每种情况提供精准干预建议。结果有助于提升临床医生对风险评估的信任度，推动其在专科资源匮乏地区的更广泛应用，将原本不透明的评分转化为透明且具指导性的洞察。
原文摘要 · Abstract (English)
Cardiovascular disease (CVD) remains one of the leading global health challenges, accounting for more than 19 million deaths worldwide. To address this, several tools that aim to predict CVD risk and support clinical decision making have been developed. In particular, the Framingham Risk Score (FRS) is one of the most widely used and recommended worldwide. However, it does not explain why a patient was assigned to a particular risk category nor how it can be reduced. Due to this lack of transparency, we present a logical explainer for the FRS. Based on first-order logic and explainable artificial intelligence (XAI) fundaments, the explainer is capable of identifying a minimal set of patient attributes that are sufficient to explain a given risk classification. Our explainer also produces actionable scenarios that illustrate which modifiable variables would reduce a patient's risk category. We evaluated all possible input combinations of the FRS (over 22,000 samples) and tested them with our explainer, successfully identifying important risk factors and suggesting focused interventions for each case. The results may improve clinician trust and facilitate a wider implementation of CVD risk assessment by converting opaque scores into transparent and prescriptive insights, particularly in areas with restricted access to specialists.
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