用混合药理动力学/机器学习模型为化疗诱导的中性的临床决策支持
Jasmine H Hughes1, Dominic M H Tong1, Vanessa Burns1
1Insight Rx, Inc., San Francisco, California, USA.
CPT: pharmacometrics & systems pharmacology
|July 28, 2023
概括
这项研究引入了一种混合的药理动力学/药理动力学 (PKPD) 和机器学习 (ML) 模型,以改善化学疗法诱导的中性质减退症的预测. 这种方法通过对接受癌症治疗的患者进行个性化风险评估来增强临床决策.
科学领域:
- 药理学和机器学习
- 瘤学和血液学 在线
背景情况:
- 目前关于使用花细胞殖民地刺激因子的指导方针依赖于化学疗法诱导的中性质衰竭的有限风险模型.
- 电子健康记录 (EHR) 提供了大量数据,可以更个性化地预测患者的风险.
研究的目的:
- 开发和评估一种混合的药理动力学/药理动力学 (PKPD) 和机器学习 (ML) 模型,以更好地预测化疗诱导的中性质减退症.
- 评估PKPD模型预测和贝叶斯参数估计对ML模型性能的影响.
主要方法:
- 开发了一种混合PKPD/ML模型,使用了9121名为淋巴瘤,乳腺或胸腺癌治疗的患者的数据.
- 该研究将混合模型的性能与独立的PKPD和ML模型进行了比较.
- 通过PKPD模型生成的合成数据被探索用于增强ML模型训练.
主要成果:
- 与PKPD (47%,33%) 或基础ML模型 (51%,31%) 相比,PKPD丰富的ML模型可以更好地预测3-4级中性质衰竭,精度更高 (61%) 和回忆 (39%).
- 用合成PKPD数据增强ML模型显示了微小的回忆改进,但没有提高精度,并且需要仔细调整以防止过度拟合.
结论:
- 混合PKPD/ML模型显示显著增强化学疗法诱导的中性质减退的预测的承诺.
- 这种方法有效地结合了PKPD模型的生理见解与ML在大型数据集上的预测能力,用于在瘤学中精确剂量.
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