学习的因果转换器 嵌入结构化医疗史记录和多源数据集成的嵌入,用于复杂疾病风险预测
Zeming Li1, Yu Xu1, Debajyoti Chowdhury2
1Department of Computer Science, Hong Kong Baptist University, Hong Kong, 999077, China.
Interdisciplinary sciences, computational life sciences
|September 17, 2025
概括
这项研究引入了一个新的框架,疾病风险预测多源整合 (MIDRP),通过整合遗传,生活方式,身体和病史数据来改善复杂疾病风险预测. MIDRP在冠状动脉疾病,2型糖尿病和乳腺癌方面取得了最先进的结果.
科学领域:
- 计算生物学是一种计算生物学.
- 医疗信息学医学信息学
- 基因组学就是基因组学.
背景情况:
- 传统的疾病风险模型通常使用有限的数据,影响准确性.
- 综合并发症和病史对于全面的风险预测至关重要.
- 现有的模型很难有效地利用复杂的病史数据.
研究的目的:
- 开发一个新的框架,疾病风险预测多源整合 (MIDRP),用于增强复杂疾病风险预测.
- 整合多样化的数据来源,包括遗传变异,生活方式因素,身体属性和病史.
- 为了利用因果变压器架构来提取细微的病史模式.
主要方法:
- 提出了疾病风险预测多源整合 (MIDRP) 框架.
- 利用因果变压器架构来分析病史记录.
- 评估了MIDRP与多个基线模型相比,使用英国冠状动脉疾病,2型糖尿病和乳腺癌的英国生物银行数据.
主要成果:
- 在三种复杂疾病中,MIDRP实现了最先进的性能.
- 获得的接收器操作特征曲线 (AUROC) 下的区域为冠状动脉疾病的0.783,2型糖尿病的0.841,乳腺癌的0.784.
- 与LDPred2,随机森林和Med-Bert等既定方法相比,证明了更高的预测准确性.
结论:
- MIDRP框架为复杂疾病风险预测提供了强大而准确的方法.
- 整合多来源数据,特别是病史数据,大大提高了预测能力.
- 因果变压器架构有效地从电子健康记录中提取有价值的模式,以改进风险评估.
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