使用贝叶斯优化机器学习模型与遗传风险得分的机器学习模型,在绝经后妇女中增强了骨质疏松性骨折预测
1Department of Biomedical Informatics (Dr. Qing Wu, Jingyuan Dai), College of Medicine, The Ohio State University, Columbus, OH 43210, United States.
概括
将遗传风险得分与机器学习模型相结合,可显著改善绝经后妇女骨折风险预测. 优化的XGBoost模型在主要骨质疏松和关节骨折方面表现出卓越的准确性.
科学领域:
- 遗传学 是一个遗传学.
- 机器学习 机器学习
- 骨质疏松症研究 骨质疏松症研究
背景情况:
- 骨质疏松性骨折对健康造成重大负担,特别是在绝经后的妇女身上.
- 目前的骨折风险预测模型在准确度上有局限性.
- 整合遗传数据为增强预测提供了一个有希望的途径.
研究的目的:
- 提高预测主要骨质疏松性骨折 (MOFs) 和部骨折 (HFs) 的准确性.
- 评估将遗传风险得分 (GRS) 与机器学习 (ML) 模型相结合的有效性.
- 使用贝叶斯优化优化ML模型以提高预测性能.
主要方法:
- 在25772名绝经后妇女中,从1,103个SNP中开发了遗传风险评分 (GRS).
- 训练并比较了使用GRS和临床风险因子 (CRF) 的四个ML模型 (SVM,随机森林,XGBoost,ANN).
- 采用贝叶斯优化来对ML模型进行微调,并考虑竞争风险 (死亡).
主要成果:
- 集成GRS的XGBoost模型与贝叶斯优化实现了91.2%的准确性和MOF二进制预测的0.739 AUC.
- 对于10年骨折风险,XGBoost的C指数为0.795,平均动态AUC为0.799.
- 与FRAX相比,XGBoost模型显示了22.6%的净重新分类改进,并在不同的群体中表现出强度.
结论:
- 将遗传特征与优化机器学习模型相结合,可显著提高骨折风险预测.
- 开发的XGBoost模型提供了一个更准确和更强大的工具,用于识别患有骨质疏松性骨折高风险的女性.
- 这种方法有可能开发新的预防性策略来治疗绝经后骨质疏松症.
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