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.

翻译：心血管疾病（CVD）仍然是全球最主要的健康挑战之一，每年导致全球超过1900万人死亡。为解决这一问题，已开发出多种旨在预测CVD风险并支持临床决策的工具。其中，弗雷明汉风险评分（FRS）是全球范围内使用最广泛且最受推荐的评估工具之一。然而，该评分既未解释患者被归入特定风险类别的原因，也未说明如何降低风险。针对这种透明度的缺失，我们提出了一种基于逻辑的FRS解释器。该解释器以一阶逻辑和可解释人工智能（XAI）基本原理为基础，能够识别足以解释特定风险分类的最小患者特征集合。我们的解释器还能生成可操作的干预场景，说明通过调整哪些可改变变量能够降低患者的风险等级。我们评估了FRS所有可能的输入组合（超过22,000个样本），并使用解释器进行测试，成功识别出关键风险因素，并为每个案例提出了针对性干预建议。这些结果有望增强临床医生的信任度，并通过将不透明的评分转化为透明且具有指导意义的见解，促进心血管疾病风险评估的更广泛应用，特别是在专科医疗资源有限的地区。