基于机器学习的预测模型对他类药物的疗效和安全性进行预测
Yu Xiong1,2, Xiaoyang Liu2,3, Qing Wang4
1Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Frontiers in pharmacology
|August 13, 2024
概括
机器学习模型预测了他类药物的疗效和安全性,改善了动脉样硬化心血管疾病 (ASCVD) 风险管理. 开发的平台有助于优化他类药物治疗决策,以获得更好的患者结果.
科学领域:
- 心血管医学 心血管医学
- 生物统计学 生物统计学
- 医疗保健中的机器学习
背景情况:
- 类固醇治疗对于降低动脉样硬化心血管疾病 (ASCVD) 风险至关重要.
- 增加胆固醇水平需要合理使用他类药物.
- 与他类药物相关的不良反应,如肝酶异常和肌肉症状 (SAMS),阻碍了广泛使用.
研究的目的:
- 开发一种对他类药物的有效性和安全性的预测模型.
- 利用现实世界的临床数据和机器学习技术.
- 创建一个基于网络的平台,用于预测他类药物的结果.
主要方法:
- 数据的预处理包括随机森林归算和边界SMOTE过量抽样.
- 博鲁塔方法用于特征选择; 7:3列车测试分割.
- 五个算法 (逻辑回归,天真贝叶斯,决策树,随机森林,梯度提升) 经过交叉验证和引导被评估.
- SHAP用于功能解释性和Web平台的开发.
主要成果:
- 随机森林算法显示出卓越的性能.
- 高AUC和准确度用于预测LDL-C目标达到 (0.883,0.868),肝酶异常 (0.964,0.964) 和肌肉疼痛/CK异常 (0.981,0.980) 的预测.
- 确定了关键预测因素:脑梗塞,TG,PLT,HDL对疗效;CRP,CK,治疗肝/肌肉不良事件的口服药物.
结论:
- 成功开发了一种基于机器学习的预测模型,用于确定他类药物的有效性和安全性.
- 这种用户友好的网络平台可以指导他类药物治疗决策,并优化治疗.
- 建议进行进一步的研究和应用,以提高他类药物治疗的利用率.
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