Deep Generative Models for Treatment Effect Inference in Healthcare

医疗保健治疗建议通常是通过研究“平均患者”和进行随机对照试验(RCT)来制定的。然而，个体对治疗的反应各不相同。目前的医疗决策往往缺乏个性化的、数据驱动的方法，导致不太理想的结果。由于顺序决策、数据需求和计算挑战，根据患者的演变特征和过去的反应量身定制治疗方案(动态治疗方案)是复杂的。此外，在临床实践中很难确定影响治疗效果的关键因素。使用电子健康记录(EHR)估计治疗的因果效应需要创新的方法来考虑患者随着时间的推移的健康状况和结果。该项目的主要目标是为各种医疗情景的临床治疗效果建模。在其他技术中，这将通过使用深度生成模型来实现。这些模型学习EHR的基本表示，并允许创建与患者的特征和时间动态非常相似的合成双胞胎1。这个拟议项目的第一个目标是开发一个个性化的治疗效果模型，基于患者的EHR的合成。拟议的模型将促进产生具有代表性的数字双胞胎，这些数字双胞胎可以作为个别患者的合成对应物，从而能够深入分析和探索个性化的医疗保健结果2。第二个研究目标将侧重于利用拟议的模型来确定和优化治疗方案。这涉及到利用数字双胞胎的表征来评估个别患者的因果治疗效果，并探索他们对不同治疗的反应。目标是建立一个决策框架，以便为各种可能的情况和医疗保健环境确定最佳的治疗策略，包括(I)初级护理、(Ii)医院病房的二级护理和(Iii)重症监护病房(ICU)的二级护理。初级保健环境是测试与逐渐发展的健康状况相关的用例的良好环境，例如高胆固醇血症等慢性病。在医院环境中，我们可以探索动态治疗方案，比如对感染使用抗生素，这方面的研究还不够深入。ICU环境中的潜在应用包括通过给予血管增压剂治疗脓毒症。该项目属于EPSRC卫生技术研究领域，专门应对挑战3，发现和加速新干预措施的开发。这项研究通过与仅从平均效果得出治疗效果的传统方法不同，展示了它的新颖性。相反，它努力为医生提供个性化的治疗建议，考虑到每个患者的独特特征。更具体地说，就目前正在探索和研究中的医学应用而言，这项研究将是新颖的，但在所采用的方法方面也是如此。例如，我们的目标是创建适用于动态治疗方案的新的个性化治疗效果模型，并将设计方法来确保预测的清晰度和可解释性。此外，我们将探索新的方法来验证该框架在现实世界医疗用例中的适用性，例如，通过执行观察性研究来复制大规模随机对照试验的研究结果。这些结果可能会增强医疗保健的决策，导致改善患者结果和量身定做的治疗。此外，这种方法通过提供一个模拟环境来评估治疗效果和确定个性化的治疗选择，为克服临床试验中的挑战提供了一条有希望的途径。

Healthcare treatment recommendations are typically formulated by studying the 'average patient' and conducting randomised control trials (RCTs). However, individual responses to treatments vary. Current healthcare decisions often lack personalised, data-driven approaches, resulting in suboptimal outcomes. Tailoring treatments to a patient's evolving characteristics and past responses (dynamic treatment regimens) is complex due to sequential decision-making, data demands, and computational challenges. Furthermore, identifying key factors influencing treatment effects in clinical practice is difficult. Estimating the causal effects of treatments using electronic health records (EHRs) requires innovative methods that account for patients' health status and outcomes over time.The primary objective of this project is to model clinical treatment effects for various medical scenarios. Among other techniques, this will be achieved by using deep generative models. These models learn the fundamental representation of EHRs and allow to create synthetic twins that closely resemble a patient's characteristics and temporal dynamics.1. The first goal of this proposed project is to develop a personalised treatment effect model, based on the synthesis of a patient's EHR. The proposed model will facilitate the generation of representative digital twins that can serve as synthetic counterparts for individual patients, enabling in-depth analysis and exploration of personalised healthcare outcomes.2. The second research goal will focus on leveraging the proposed model to identify and optimise treatment regimes. This involves utilising the digital twins' representation to estimate the causal treatment effects for individual patients and exploring their responses to different treatments. The objective is to develop a decision-making framework that enables the identification of optimal treatment strategies for a variety of possible scenarios and healthcare settings including (i) primary care, (ii) secondary care in the hospital wards and (iii) secondary care in the intensive care unit (ICU). The primary care setting is a promising environment for testing use cases related to health conditions that progress gradually, such as chronic diseases like hypercholesterolemia. In the hospital setting, we could explore dynamic treatment regimens like administering antibiotics for infections, which have been under-studied. Potential applications in an ICU setting include treating sepsis through administering vasopressors.This project falls within the EPSRC Health Technologies research area and specifically addresses Challenge 3, discovering and accelerating the development of new interventions. The research showcases its novelty by departing from the conventional approach of deriving treatment effects solely from average effects. Instead, it strives to empower doctors with personalised treatment recommendations that consider the unique characteristics of each patient. More specifically, the research will be novel in terms of the medical applications that are being explored and have been under-studied to date, but also in terms of methods deployed. For instance, we aim to create new personalised treatment effect models applicable to dynamic treatment regimens and will devise methods to ensure the clarity and interpretability of predictions. Furthermore, we will investigate new ways to validate the applicability of the framework to real-world medical use-cases, e.g., by performing an observational study to reproduce findings from a large-scale RCT study. The outcomes will potentially enhance decision-making in healthcare, leading to improved patient outcomes and tailored treatments. Additionally, this approach offers a promising avenue to overcome challenges in clinical trials by providing a simulated environment to assess treatment efficacy and identify personalised treatment options.