Sanchez Pedro, Voisey Jeremy P, Xia Tian, Watson Hannah I, O'Neil Alison Q, Tsaftaris Sotirios A
School of Engineering, University of Edinburgh, Edinburgh, UK.
AI Research, Canon Medical Research Europe, Edinburgh, Lothian, UK.
R Soc Open Sci. 2022 Aug 3;9(8):220638. doi: 10.1098/rsos.220638. eCollection 2022 Aug.
Causal machine learning (CML) has experienced increasing popularity in healthcare. Beyond the inherent capabilities of adding domain knowledge into learning systems, CML provides a complete toolset for investigating how a system would react to an intervention (e.g. outcome given a treatment). Quantifying effects of interventions allows actionable decisions to be made while maintaining robustness in the presence of confounders. Here, we explore how causal inference can be incorporated into different aspects of clinical decision support systems by using recent advances in machine learning. Throughout this paper, we use Alzheimer's disease to create examples for illustrating how CML can be advantageous in clinical scenarios. Furthermore, we discuss important challenges present in healthcare applications such as processing high-dimensional and unstructured data, generalization to out-of-distribution samples and temporal relationships, that despite the great effort from the research community remain to be solved. Finally, we review lines of research within causal representation learning, causal discovery and causal reasoning which offer the potential towards addressing the aforementioned challenges.
因果机器学习（CML）在医疗保健领域越来越受欢迎。除了将领域知识添加到学习系统的固有能力之外，CML还提供了一套完整的工具集，用于研究系统对干预措施（例如给予治疗后的结果）的反应。量化干预措施的效果可以在存在混杂因素的情况下做出可操作的决策，同时保持稳健性。在这里，我们探讨如何通过利用机器学习的最新进展将因果推断纳入临床决策支持系统的不同方面。在本文中，我们使用阿尔茨海默病来创建示例，以说明CML在临床场景中的优势。此外，我们讨论了医疗保健应用中存在的重要挑战，例如处理高维和非结构化数据、对分布外样本的泛化以及时间关系，尽管研究界付出了巨大努力，但这些挑战仍有待解决。最后，我们回顾了因果表示学习、因果发现和因果推理方面的研究方向，这些研究方向有望应对上述挑战。
