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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
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概括
此摘要是机器生成的。

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.

关键词:
深度学习是一种深度学习.基因组广泛的关联研究研究.病史记录 记录病史记录多基因风险评分多基因风险评分.单个核酸的多态性.

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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.