发表机构
Kyoto University Graduate School of Medicine; Cancerscan Inc.; Shiga University（京都大学医学研究科; Cancerscan 公司; 滋贺大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究利用交叉注意力机制整合医疗索赔数据中的诊断与治疗关系，生成更具代表性的特征，显著提升了主要不良心血管事件（MACE）的预测性能，ROC-AUC达0.7720，优于多个基准模型。
AI 中文摘要
医疗索赔数据包含财务细节，如患者的费用和账单信息，以及临床信息，如就诊于医疗机构的患者的诊断和治疗。近年来，人们认识到可以利用医疗索赔数据构建大型数据库用于医学研究。然而，这些数据集中的临床信息通常在医学上是非结构化的，限制了其在综合分析中的应用。本研究提升了主要不良心血管事件（MACE）预测模型的性能，MACE是全球主要的死亡原因。预测MACE的模型对临床实践指南至关重要。我们利用交叉注意力机制开发了一种方法，有效加权诊断与治疗之间的关系。通过有效表示医疗索赔数据中包含的临床信息，该方法为预测MACE生成了更具代表性的特征。我们提出的基于交叉注意力模型的ROC-AUC得分为0.7720，高于其他基准模型，包括传统的动脉粥样硬化性心血管疾病模型、轻量梯度提升机和基于自注意力的模型。这些结果表明，使用交叉注意力机制整合医疗索赔数据的临床结构显著提升了预测模型的性能。
英文摘要
Medical claims data comprise the financial details, including the expenses and billing information, as well as the clinical information, such as the diagnoses and treatments, of patients visiting medical facilities. Recently, it has been acknowledged that large databases can be constructed from medical claims data for medical research purposes. However, the clinical information within these datasets is often medically unstructured, limiting its application in comprehensive analyses. This study enhances predictive model performance for major adverse cardiovascular events (MACE), a leading cause of death worldwide. Models that predict MACE are crucial to clinical practice guidelines. We utilize a cross-attention mechanism to develop a method that effectively weights the relationships between diagnoses and treatments. Effectively repre- senting the clinical information contained in medical claims data, this approach generates more representative features for predicting MACE. The ROC-AUC score of our proposed cross-attention-based model was 0.7720, higher than other benchmark models including the conventional atherosclerotic cardiovascular disease model, the light gradient boosting machine, and a self-attention-based model. These results indicate that integrating the clinical structure of medical claims data using a cross-attention mechanism significantly enhances the performance of predictive models.
Comments13pages, 3 figures. Accepted to KDD 2024 AIDSH workshop
