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混合机制建模和深度学习用于在免疫检查点抑制剂免疫治疗后个性化生存预测
Joseph D Butner1,2,3, Prashant Dogra4,5, Caroline Chung6,7
1Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. jdbutner@mdanderson.org.
NPJ systems biology and applications
|August 14, 2024
概括
这项研究结合了机械模型和深度学习,以预测免疫检查点抑制剂 (ICI) 治疗期间患者的生存率. 混合方法提高了使用临床和模型衍生数据的预测准确性.
科学领域:
- 计算生物学是一种计算生物学.
- 免疫疗法研究研究
- 机器学习在医学中的应用
背景情况:
- 免疫检查点抑制剂 (ICI) 疗法彻底改变了癌症治疗.
- 预测个体患者对ICI治疗的反应仍然是一个挑战.
- 现有的预测模型往往难以整合不同的数据类型.
研究的目的:
- 开发一种混合预测模型,结合机械学和深度学习方法.
- 为了提高在ICI免疫疗法下患者生存概率的预测.
- 为了利用可测量的临床数据和非直接可测量的机械洞察力.
主要方法:
- 一种混合方法,将预测力学建模与深度学习相结合.
- 开发一个深度学习的时间到事件预测模型.
- 在来自93名患者的机械学和临床数据组合数据集上训练模型.
主要成果:
- 与仅使用机械学或临床数据的模型相比,混合模型表现出更高的每患者预测准确性.
- 使用事件时间一致性,布里尔得分和负二项式日志概率来评估性能.
- 特性重要性分析证实了临床和模型衍生参数的显著贡献.
结论:
- 将机械模型与深度学习相结合,为预测ICI治疗结果提供了一个强大的策略.
- 混合方法有效地整合了各种数据源,以提高预测性能.
- 这种方法对个性化癌症治疗策略有前途.
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