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arXiv 2502.17049cs.AIcs.LG

TabulaTime：一种通过环境与临床数据整合推进急性冠脉综合征预测的新型多模态深度学习框架

TabulaTime: A Novel Multimodal Deep Learning Framework for Advancing Acute Coronary Syndrome Prediction through Environmental and Clinical Data Integration

Department of Computing, and Mathematics, Manchester Metropolitan University（计算机与数学系，曼彻斯特 Metropolitan 大学）；Blackpool Teaching Hospitals NHS Foundation Trust（布莱克浦教学医院 NHS 基础信托）；Northern Care Alliance, NHS Foundation Trust（北方护理联盟，NHS 基础信托）；Department of Engineering, Manchester Metropolitan University（工程系，曼彻斯特 Metropolitan 大学）；Faculty of Business and Law, Manchester Metropolitan University（商业与法律学院，曼彻斯特 Metropolitan 大学）；Faculty of Medical Sciences, Newcastle University（医学科学学院，纽卡斯尔大学）

Xin Zhang, Liangxiu Han, Stephen White, Saad Hassan, Philip A Kalra, James Ritchie, Carl Diver, Jennie Shorley

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AI总结：

提出多模态深度学习框架TabulaTime，通过整合临床表格数据与时间序列空气污染数据预测急性冠脉综合征，利用PatchRWKV模块提取时序模式，预测准确率较传统模型提升超20%。

摘要：

急性冠脉综合征（ACS），包括ST段抬高型心肌梗死（STEMI）和非ST段抬高型心肌梗死（NSTEMI），仍是全球主要死因。传统心血管风险评分主要依赖临床数据，常忽视显著影响心脏健康的环境因素（如空气污染）。此外，将复杂时间序列环境数据与临床记录整合颇具挑战。我们提出TabulaTime，一种通过结合临床风险因素与空气污染数据来增强ACS风险预测的多模态深度学习框架。TabulaTime具有三项关键创新：首先，将时间序列空气污染数据与临床表格数据整合以提升预测准确率；其次，其PatchRWKV模块自动提取复杂时间模式，克服传统特征工程局限并保持线性计算复杂度；第三，注意力机制通过揭示临床与环境因素间交互作用增强可解释性。实验结果表明，与CatBoost、Random Forest和LightGBM等传统模型相比，TabulaTime将预测准确率提升超20%，仅空气污染数据就贡献了超10%的提升。特征重要性分析确定了关键预测因子，包括既往心绞痛、收缩压、PM10和NO2。总体而言，TabulaTime连接了临床与环境洞察，支持个性化预防策略并指导公共卫生政策以降低ACS风险。

英文摘要：

Acute Coronary Syndromes (ACS), including ST-segment elevation myocardial infarctions (STEMI) and non-ST-segment elevation myocardial infarctions (NSTEMI), remain a leading cause of mortality worldwide. Traditional cardiovascular risk scores rely primarily on clinical data, often overlooking environmental influences like air pollution that significantly impact heart health. Moreover, integrating complex time-series environmental data with clinical records is challenging. We introduce TabulaTime, a multimodal deep learning framework that enhances ACS risk prediction by combining clinical risk factors with air pollution data. TabulaTime features three key innovations: First, it integrates time-series air pollution data with clinical tabular data to improve prediction accuracy. Second, its PatchRWKV module automatically extracts complex temporal patterns, overcoming limitations of traditional feature engineering while maintaining linear computational complexity. Third, attention mechanisms enhance interpretability by revealing interactions between clinical and environmental factors. Experimental results show that TabulaTime improves prediction accuracy by over 20% compared to conventional models such as CatBoost, Random Forest, and LightGBM, with air pollution data alone contributing over a 10% improvement. Feature importance analysis identifies critical predictors including previous angina, systolic blood pressure, PM10, and NO2. Overall, TabulaTime bridges clinical and environmental insights, supporting personalized prevention strategies and informing public health policies to mitigate ACS risk.

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。

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