Health Data Science CDT

多发性硬化症(MS)是一种慢性衰弱疾病，全世界约有280万人受到影响。多发性硬化症是复杂的、不可预测的、目前无法治愈的疾病，只能通过旨在提高生活质量和减缓疾病进展的疾病修正治疗(DMT)来管理。尽管有超过25种批准的DMT可用，但在了解患者对这些治疗的反应的变异性方面仍存在显著差距，这影响了MS管理的有效性。我们可以访问独特的诺华-牛津MS(NO.MS)数据集，该数据集收集了诺华、牛津大数据研究所(BDI)和MS医生合作的34,000多名个人的纵向数据。利用这一数据集，该项目旨在开发复杂的统计机器学习方法，专注于预测性和生成性模型，通过表征和预测个体治疗反应和绘制疾病轨迹来促进我们对多发性硬化的理解。具体地说，我们实现并扩展了贝叶斯加法回归树(BART)，以对个体治疗效果进行因果估计，将扫描仪效应与生物因素分开建模，并引入患者特定的随机效应来解释治疗反应随时间的变化。这种创新的方法使我们能够表征患者之间治疗结果的差异性，并识别驱动这种异质性的生物标记物，最终为新治疗的开发提供信息。然后，我们进一步改进这些模型，以产生更准确的个体治疗效果(ITE)估计，扩展到多变量结果，并探索使用全脑数据来提取更丰富的生物学特征，从而提高预测准确性。通过利用这些精细化的估计和生物标记物，我们的目标是将患者分组为治疗效果组。此外，我们建立了创新的动态和因果模型来预测疾病的未来进程，将我们的模型扩展到生存/事件发生时间结果，并整合干预时间来分析它们对疾病进展的影响。最终，我们的目标是开发新的生成性模型，能够预测每个患者的多个潜在疾病轨迹，基于单个生物标记物、治疗历史和潜在的未来干预进行动态调整，有效地为每个患者创建“数字双胞胎”。我们开发的模型能够处理横断面和纵向数据，适应单变量和多变量的结果，以及管理数据缺失，并结合不确定性量化来确保可靠的预测。丰富而广泛的纵向NO.MS数据集为开发和改进我们的方法提供了一个独特的机会，使我们能够推进MS研究的最先进水平。这项工作的潜在影响是深远的，因为它旨在减少MS的不可预测性，最终导致更知情的治疗决策和改善患者结果。此外，我们预计我们的创新方法可以应用于不同疾病的研究。该项目属于EPSRC人工智能技术和临床技术(不包括成像)研究领域，为医疗保健技术主题做出贡献。

Multiple Sclerosis (MS) is a chronic and debilitating disease that affects approximately 2.8 million individuals worldwide. MS is complex, unpredictable and currently incurable, only managed through disease-modifying treatments (DMTs) that aim to improve life quality and slow disease progression. Despite the availability of over 25 approved DMTs, there remains a significant gap in understanding the variability in patient responses to these treatments, which impacts the effectiveness of MS management.We have access to the unique Novartis-Oxford MS (NO.MS) dataset, which is a collection of longitudinal data from over 34,000 individuals from a collaboration between Novartis, the Oxford Big Data Institute (BDI), and MS physicians. Leveraging this dataset, this project aims to develop sophisticated statistical machine learning methods,focusing on predictive and generative models, to advance our understanding of MS by characterising and predicting individual treatment responses and mapping disease trajectories. Specifically, we implement and extend Bayesian Additive Regression Trees (BART) to causally estimate individual treatment effects, modelling scanner effects separately from biological factors and introducing patient-specific random effects to account for variations in treatment responses over time. This innovative approach allows us to characterise the variability in treatment outcomes between patients and identify biomarkers driving this heterogeneity, ultimately informing the development of new treatments. We then further refine these models to produce more accurate estimates of individual treatment effects (ITE), extending to multivariate outcomes and exploring the use of whole-brain data to extract richer biological features, thereby enhancing predictive accuracy. By leveraging these refined estimates and biomarkers, we aim to cluster patients into treatment efficacy groups. Additionally, we establish innovative dynamic and causal models to predict the future course of the disease, extending our models to survival/time-to-event outcomes and integrating intervention times to analyse their impact on disease progression. Ultimately, we aim to develop novel generative models capable of predicting multiple potential disease trajectories for each patient, dynamically adjusting based on individual biomarkers, treatment history, and potential future interventions, effectively creating "Digital Twins" for each patient.We develop models capable of handling both cross-sectional and longitudinal data, accommodating univariate and multivariate outcomes, as well as managing data missingness, and we incorporate uncertainty quantification to ensure reliable predictions. The rich and extensive longitudinal NO.MS dataset provides a unique opportunity to develop and refine our methods, allowing us to advance the state-of-the-art in MS research.The potential impact of this work is profound, as it aims to reduce the unpredictability of MS, ultimately leading to better-informed treatment decisions and improved patient outcomes. Moreover, we anticipate our innovative methods could be applied to the study of different diseases.This project falls within the EPSRC Artificial Intelligence technologies and Clinical Technologies (excluding imaging) research areas, contributing to the healthcare technology theme.