基于机器学习的欧洲儿童健康环境临床风险评分
Jean-Baptiste Guimbaud1,2,3,4, Alexandros P Siskos5, Amrit Kaur Sakhi6
1ISGlobal, Barcelona, Spain.
Communications medicine
|May 23, 2024
概括
机器学习创建了早期生命暴露组风险评分 (ECRS) 来预测儿童健康. ECRS确定了影响精神,心脏代谢和呼吸系统健康结果的关键环境和分子因素.
科学领域:
- 环境流行病学环境流行病学
- 机器学习在健康研究中的应用
- 个性化医疗是个性化的医疗.
背景情况:
- 早期的生活环境压力因素与慢性疾病有关.
- 现有的环境风险评分 (ERS) 在捕捉早期生命决定因素方面存在局限性.
- 需要新的机器学习方法来评估累积的早期生命暴露.
研究的目的:
- 开发和验证儿童健康结果的早期生命暴露组风险评分 (ECRS).
- 利用机器学习整合各种数据类型 (环境,分子,临床).
- 确定儿童行为,代谢和呼吸系统健康的关键预测特征.
主要方法:
- 分析了来自欧洲出生队伍 (HELIX) 的1622对母婴对.
- 使用LASSO,随机森林和XGBoost.计算环境临床风险得分 (ECRS).
- 使用XGBoost ECRS的Shapley值进行特征重要性和相互作用分析.
主要成果:
- ECRS解释了精神健康的13%,心脏代谢健康的50%,呼吸系统健康的4%.
- 机器学习方法之间没有发现预测性能的显著差异.
- 关键预测因素包括母亲的压力,噪音,生活方式,蛋白质/代谢特征和儿童的BMI.
结论:
- 在高风险儿童中,ECRS显示出识别可操作的非遗传性风险因素的潜力.
- 使用ECRS的综合方法可以为个性化预防医学提供信息.
- 这些方法对流行病学研究和未来的医疗保健范式有价值.
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