通过推断家族血统来增强患者代表性学习,可以改善疾病风险预测
Xiayuan Huang1, Jatin Arora2, Abdullah Mesut Erzurumluoglu2
1Department of Biostatistics, Yale University School of Public Health, New Haven, CT 06510, United States.
Journal of the American Medical Informatics Association : JAMIA
|December 26, 2024
概括
在电子健康记录 (EHR) 中建模家庭健康史显著改善了疾病风险预测. 我们的ALIGATEHR模型捕捉了遗传影响和疾病依赖性,增强了患者代表性学习.
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
- 机器学习在医疗保健中的应用
背景情况:
- 电子健康记录 (EHR) 对医疗保健研究具有价值.
- 家庭健康史是疾病的关键预测因素,但在电子健康史分析中经常被低估.
- 目前的方法将患者视为独立者,忽视了家庭关系.
研究的目的:
- 开发一种用于将家庭病史纳入EHR分析的新方法.
- 通过考虑遗传和环境因素来改善疾病风险预测.
- 增强患者代表性学习,用于预测和可解释的电子健康记录建模.
主要方法:
- 阿利加特:一个图表注意力网络模型,包含推断的家庭关系.
- 增强了基于注意力的医学本体学,用于复杂的因子建模.
- 使用纵向EHR诊断数据将患者的风险与亲属的个人资料联系起来.
主要成果:
- 明确模拟家庭关系显著改善了在各种疾病中预测疾病风险.
- 阿里加特的注意力机制成功地捕捉了遗传疾病的各个方面.
- 该模型有效地区分克罗恩病和性结肠炎,两个炎症性肠道疾病.
结论:
- 家庭关系至关重要,应该纳入EHR研究.
- ALIGATEHR显示了提高预测性和可解释性EHR建模的巨大潜力.
- 该方法提供了对疾病病因和患者风险的更全面的了解.
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