From Real-World Patient Data to Individualized Treatment Effects Using Machine Learning: Current and Future Methods to Address Underlying Challenges
From Real-World Patient Data to Individualized Treatment Effects Using Machine Learning: Current and Future Methods to Address Underlying Challenges
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DOI：
10.1002/cpt.1907
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发表时间：
2020-06-28
影响因子：
6.7
通讯作者：
van der Schaar, Mihaela
中科院分区：
文献类型：
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作者：
Bica, Ioana;Alaa, Ahmed M.;van der Schaar, Mihaela
临床决策需要有证据支持，即治疗对个体患者有益。尽管随机对照试验（RCT）是测试和引入新药的金标准，但由于关注的是与确定疗效和安全性相比标准治疗的特定问题，因此它们不能提供最终预期治疗人群异质性的完整特征。相反，真实世界的观察数据，如电子健康记录（EHR），包含大量关于异质性患者及其对治疗反应的临床信息。在本文中，我们介绍了使用观察数据训练机器学习方法来估计个体化治疗效果并提出治疗建议的主要机遇和挑战。我们描述了用于因果推理的最先进的机器学习方法的建模选择，该方法是为估计横截面和纵向设置中的治疗效果而开发的。此外，我们强调了未来的研究方向，这些方向可能会导致充分发挥EHR和机器学习的潜力，以提供个性化的治疗建议。我们还讨论了如何从随机对照试验和药理学和定量系统药理学方法的实验数据，不仅可以用来改善机器学习方法，而且还提供了验证它们的方法。这些未来的研究方向将要求我们跨学科合作，将基于RCT和已知疾病过程、生理学和药理学的模型纳入这些基于EHR的机器学习模型中，以充分优化这些数据提供的机会。
Clinical decision making needs to be supported by evidence that treatments are beneficial to individual patients. Although randomized control trials (RCTs) are the gold standard for testing and introducing new drugs, due to the focus on specific questions with respect to establishing efficacy and safety vs. standard treatment, they do not provide a full characterization of the heterogeneity in the final intended treatment population. Conversely, real-world observational data, such as electronic health records (EHRs), contain large amounts of clinical information about heterogeneous patients and their response to treatments. In this paper, we introduce the main opportunities and challenges in using observational data for training machine learning methods to estimate individualized treatment effects and make treatment recommendations. We describe the modeling choices of the state-of-the-art machine learning methods for causal inference, developed for estimating treatment effects both in the cross-section and longitudinal settings. Additionally, we highlight future research directions that could lead to achieving the full potential of leveraging EHRs and machine learning for making individualized treatment recommendations. We also discuss how experimental data from RCTs and Pharmacometric and Quantitative Systems Pharmacology approaches can be used to not only improve machine learning methods, but also provide ways for validating them. These future research directions will require us to collaborate across the scientific disciplines to incorporate models based on RCTs and known disease processes, physiology, and pharmacology into these machine learning models based on EHRs to fully optimize the opportunity these data present.