Scalable Bayesian Statistical Machine Learning for Multi-modal Data with Applications to Multiple Sclerosis

多模态数据的可扩展贝叶斯统计机器学习及其在多发性硬化症中的应用

• 批准号: 2740724
• 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-2740724
• 关键词: Scalable; Bayesian; Statistical; Machine; Learning

Within this project, we undertake the challenge of effectively managing extensive and complex datasets, as exemplified by the NO.MS clinical dataset. The primary objective is to streamline the inherent complexity of these datasets by uncovering underlying latent variables. This simplification is particularly vital when contending with high-dimensional data, where the challenge lies in distilling an abundance of dimensions into a more concise set of broader covariates that remain accurate representations of the data. This concept of distilling latent variables extends its relevance to diverse fields beyond our immediate study.The NO.MS dataset, provided by Novartis, serves as a focal point of interest. It encompasses a wealth of data on individuals affected by multiple sclerosis, distinguishing itself as one of the largest datasets of its kind. This distinction arises from the inclusion of numerous MRI brain scans, contributing to its high dimensionality due to the myriad of pixels within each scan. Consequently, this dataset bears significant potential for unravelling insights into the disease. However, from an analytical standpoint, it presents considerable challenges. This complexity stems from the dataset's amalgamation of discrete data, such as disability scores, and continuous data, exemplified by the pixel values within the MRI scans. Furthermore, the dataset draws from multiple studies, each capturing distinct facets of patient visits, resulting in a substantial amount of missing data.The project's core objectives comprise of two pivotal contributions:First and foremost, we aim to construct a model that can effectively unveil an interpretable representation of the lower-dimensional latent space. Our approach relies heavily on Bayesian statistics, a statistical framework that integrates prior beliefs into modelling, subsequently updating them based on the incoming data. This model must possess the versatility to accommodate both continuous and discrete data, offering a solution for datasets like NO.MS. Furthermore, we prioritize scalability, recognizing the impracticality of conventional methods for managing large, high-dimensional datasets, such as the Novartis multiple sclerosis data. In addition, our model should autonomously determine the optimal number of latent variables required to represent the data accurately. While existing models may address individual aspects of these challenges, the unique aspect of our approach is the integration of solutions into a cohesive whole.Secondly, we intend to apply this comprehensive model to the NO.MS dataset to deepen our understanding of multiple sclerosis. This can be achieved by analysing the latent factors unveiled, in collaboration with medical experts. Additionally. These latent factors furnish a simplified representation of the data, which can, in turn, be employed in conjunction with more computationally intensive models. This streamlined representation enhances the efficiency of our analyses compared to conventional approaches.This project falls within the EPSRC Statistics and Applied Probability research area and is carried out in collaboration with Novartis, it is supervised by Dr Habib Ganjgahi, Prof Tom Nichols and Prof Chris Holmes.

在这个项目中，我们承担了有效管理广泛而复杂的数据集的挑战，例如NO.MS临床数据集。主要目标是通过揭示潜在的潜在变量来简化这些数据集的固有复杂性。当处理高维数据时，这种简化尤为重要，其中的挑战在于将丰富的维度提炼成一组更简洁的更广泛的协变量，这些协变量仍然是数据的准确表示。这种提取潜在变量的概念将其相关性扩展到我们当前研究之外的各个领域。诺华公司提供的NO.MS数据集是我们感兴趣的焦点。它包含了大量关于受多发性硬化症影响的个人的数据，使其成为同类中最大的数据集之一。这种区别来自于包含大量的MRI脑部扫描，由于每个扫描内的无数像素，导致其高维数。因此，该数据集具有揭示疾病的重要潜力。然而，从分析的角度来看，它提出了相当大的挑战。这种复杂性源于数据集的离散数据（如残疾评分）和连续数据（如MRI扫描中的像素值）的合并。此外，该数据集来自多个研究，每个研究都捕捉了患者就诊的不同方面，导致大量数据缺失。该项目的核心目标包括两个关键贡献：首先，我们的目标是构建一个模型，可以有效地揭示低维潜在空间的可解释表示。我们的方法在很大程度上依赖于贝叶斯统计，这是一种统计框架，将先前的信念整合到建模中，随后根据传入的数据更新它们。该模型必须具有适应连续和离散数据的多功能性，为NO.MS等数据集提供解决方案。此外，我们优先考虑可扩展性，认识到管理大型高维数据集（如诺华公司多发性硬化症数据）的传统方法不切实际。此外，我们的模型应该自主确定准确表示数据所需的潜在变量的最佳数量。虽然现有的模型可以解决这些挑战的各个方面，但我们方法的独特之处在于将解决方案整合成一个有凝聚力的整体。其次，我们打算将这个综合模型应用于NO.MS数据集，以加深我们对多发性硬化症的理解。这可以通过与医学专家合作分析所揭示的潜在因素来实现。另外。这些潜在因素提供了数据的简化表示，反过来，可以与计算密集型模型结合使用。与传统方法相比，这种简化的表示方法提高了我们分析的效率。该项目福尔斯属于EPSRC统计和应用概率研究领域，并与诺华公司合作进行，由Habib Ganjgahi博士，Tom Nichols教授和Chris Holmes教授监督。