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人工智能模型用于预测乳腺癌患者心血管疾病风险,使用电子健康记录和计算机断层扫描扫描

Isha Shah¹, Sarah Lucas², Reece Walsh³

¹BC Cancer - Kelowna, 399 Royal Ave, Kelowna, BC V1Y 5L3, Canada; Department of Computer Science, Mathematics, Physics and Statistics, The University of British Columbia, 3333 University Way, Kelowna, BC V1V 1V7, Canada.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology

|February 27, 2026

概括

一个新的深度学习模型使用CT扫描和EHR数据准确预测乳腺癌患者心血管疾病 (CVD) 死亡率. 这种融合模型可以在癌症治疗期间更好地提前检测心血管疾病风险.

科学领域:

在瘤学瘤学.
心脏病学心脏病学
人工智能在医学中的应用

背景情况:

心血管疾病 (CVD) 是全球和癌症幸存者死亡的主要原因.
早期发现心血管疾病对于因心脏毒性风险而接受治疗的乳腺癌患者至关重要.

研究的目的:

开发和评估一种融合深度学习模型,用于预测乳腺癌患者心血管疾病死亡率.
用计算机断层扫描 (CT) 扫描和电子健康记录 (EHR) 来评估模型的性能.

主要方法:

一个融合深度学习模型被开发和测试在一个大队列 (23,067名患者).
该模型整合了来自CT扫描 (∼500万片) 和EHR文件 (∼600,000) 的数据.

主要成果:

融合模型实现了高预测性能.
曲线下的面积 (AUC) 是0.946 (95% CI:0.9390.950).
准确度为0.93 (95% CI:0.920.94). 准确度为0.93 (95% CI:0.920.94). 准确度是0.93 (95% CI:0.920.94). 准确度是0.93 (95% CI:0.920.94). 准确度是0.93 (95% CI:0.920.94). 准确度是0.93 (95% CI:0.920.94).

结论:

融合模型有效地将医疗图像和文本数据结合起来,用于增强的预测任务.
这种方法在预测乳腺癌患者心血管疾病死亡率方面具有很高的准确性.

关键词:

人工智能的人工智能是人工智能.乳腺癌乳腺癌乳腺癌心血管疾病和死亡风险.计算机断层扫描 (CT) 是一种计算机断层扫描.电子健康记录电子健康记录放射治疗疗法放射治疗疗法

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