Deep Generative Models for Treatment Effect Inference in Healthcare

用于医疗保健治疗效果推断的深度生成模型

• 批准号: 2721961
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2721961
• 关键词: Deep; Generative; Models; Treatment; Effect

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.

医疗保健治疗建议通常通过研究“普通患者”和进行随机对照试验（RCT）来制定。然而，个体对治疗的反应各不相同。目前的医疗保健决策往往缺乏个性化的数据驱动方法，导致次优结果。由于顺序决策、数据需求和计算挑战，根据患者不断变化的特征和过去的反应（动态治疗方案）定制治疗是复杂的。此外，在临床实践中识别影响治疗效果的关键因素是困难的。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，这些方法可以解释患者随着时间的推移的健康状况和结果。本项目的主要目标是模拟各种医疗场景的临床治疗效果。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，并允许创建与患者特征和时间动态非常相似的合成双胞胎。该项目的第一个目标是根据患者的EHR合成，开发个性化的治疗效果模型。该模型将促进代表性数字双胞胎的生成，这些双胞胎可以作为个体患者的合成对应物，从而能够深入分析和探索个性化的医疗保健结果。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。这涉及利用数字双胞胎的代表来估计个体患者的因果治疗效果，并探索他们对不同治疗的反应。其目标是制定一个决策框架，以便为各种可能的情况和医疗保健环境确定最佳治疗策略，包括（i）初级保健，（ii）医院病房的二级保健和（iii）重症监护室（ICU）的二级保健。初级保健环境是一个很有前途的环境，可以测试与逐渐发展的健康状况相关的用例，例如高胆固醇血症等慢性疾病。在医院环境中，我们可以探索动态治疗方案，如对感染使用抗生素，这一点尚未得到充分研究。在ICU环境中的潜在应用包括通过给予血管加压药治疗脓毒症。该项目福尔斯EPSRC健康技术研究领域，专门解决挑战3，发现和加速新干预措施的开发。这项研究展示了它的新奇，脱离了传统的方法，仅从平均效果得出治疗效果。相反，它致力于为医生提供个性化的治疗建议，考虑每个患者的独特特征。更具体地说，这项研究将是新颖的，在医学应用方面，正在探索和已经研究到目前为止，但也在部署的方法。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，并将设计方法以确保预测的清晰度和可解释性。此外，我们将研究新的方法来验证该框架对现实世界医疗用例的适用性，例如，通过进行观察性研究，重现大规模RCT研究的结果。这些结果将有可能增强医疗保健决策，从而改善患者的治疗效果和量身定制的治疗方法。此外，这种方法提供了一个有希望的途径，通过提供一个模拟的环境来评估治疗效果和确定个性化的治疗方案，以克服临床试验中的挑战。