EPSRC Centre for Mathematics of Precision Healthcare

医学正在个人和人口层面同时发生变化：我们拥有前所未有的工具，可以精确监测和干预个人健康，我们还拥有高分辨率的行为和社会数据。精准医学寻求部署对每个患者的特定遗传、生活方式和环境情况敏感的疗法：了解如何最好地利用每个患者的这些众多特征是一个深刻的数学挑战。我们建议在帝国理工的数学，计算和生物医学优势的基础上，围绕数据丰富的精确医疗保健和公共卫生的多尺度网络的主题，创建一个精确医疗保健数学中心。我们的中心建议使用数学将个人层面的精准医疗与公共卫生统一起来，将高维个人数据和精细干预置于其社交网络环境中。个人健康不能与其行为和社会背景分开;例如，针对癌症的高度针对性的干预措施可能会因与社会网络结构共同变化的饮食行为引起的代谢疾病而受到破坏。无论我们是要解决慢性疾病还是晚年疾病，我们都必须同时考虑个人的联合基质及其行为和疾病传播的社会网络。我们建议在拟议的中心下解决相关的数学挑战，使帝国在网络和动力学，随机过程和分析，控制和优化，推理和数据表示方面的数学研究具有特殊优势，以制定和分析个人层面个性化医疗和公共卫生界面的数学问题，特别是对疾病进展和传播、控制性干预和医疗保健提供的数据丰富的表征，将精确干预置于更广泛的背景下。该计划将在核心研究项目上启动和持续，并将通过公开征集共同资助项目和启动计划来扩大其主题和研究人员组合。我们的首批项目将利用帝国医疗的医疗和临床资源，包括：（i）癌症患者的疾病状态和连续入院的患者旅程;（ii）心肌病和（iii）痴呆和合并症的多组学数据和成像表征;（iv）来自公共卫生的流行病学和流行病模拟数据的国家人口动态;社交网络和（v）健康信念和（vi）健康政策辩论。最初的核心项目将建立在嵌入式计算能力和数据专业知识的基础上，因此将集中于数学方法的开发，包括：稀疏状态空间方法，用于使用离散空间中的转换图表征高维数据中的疾病进展;时变网络和流行病数据的控制;几何相似性图，用于连接疾病进展的成像和组学数据;随机过程和社区检测从NHS患者数据婚礼行为和社会网络数据与个人健康指标;统计学习分层医学的分析。用于满足这些要求的数学技术将需要将动力学和随机系统的成分与图论概念、稀疏统计学习、推理和优化等结合起来，其中包括联合收割机。该中心将由数学领导，但该中心的研究人员跨越数学，生物医学，临床和计算专业知识。

Medicine is undergoing a simultaneous shift at the levels of the individual and the population: we have unprecedented tools for precision monitoring and intervention in individual health and we also have high-resolution behavioural and social data. Precision medicine seeks to deploy therapies that are sensitive to the particular genetic, lifestyle and environmental circumstances of each patient: understanding how best to use these numerous features about each patient is a profound mathematical challenge. We propose to build upon the mathematical, computational and biomedical strengths at Imperial to create a Centre for the Mathematics of Precision Healthcare revolving around the theme of multiscale networks for data-rich precision healthcare and public health. Our Centre proposes to use mathematics to unify individual-level precision medicine with public health by placing high-dimensional individual data and refined interventions in their social network context. Individual health cannot be separated from its behavioural and social context; for instance, highly targeted interventions against a cancer can be undermined by metabolic diseases caused by a dietary behaviour which co-varies with social network structure. Whether we want to tackle chronic disease or the diseases of later life, we must simultaneously consider the joint substrates of the individual together with their social network for transmission of behaviour and disease. We propose to tackle the associated mathematical challenges under the proposed Centre bringing to bear particular strengths of Imperial's mathematical research in networks and dynamics, stochastic processes and analysis, control and optimisation, inference and data representation, to the formulation and analysis of mathematical questions at the interface of individual-level personalised medicine and public health, and specifically to the data-rich characterisation of disease progression and transmission, controlled intervention and healthcare provision, placing precision interventions in their wider context. The programme will be initiated and sustained on core research projects and will expand its portfolio of themes and researchers through open calls for co-funded projects and pump-priming initiatives.Our initial set of projects will engage healthcare and clinical resources at Imperial including: (i) patient journeys for disease states in cancer and their successive hospital admissions; multi-omics data and imaging characterisations of (ii) cardiomyopathies and (iii) dementia and co-morbidities; (iv) national population dynamics for epidemiological and epidemics simulation data from Public Health; social networks and (v) health beliefs and (vi) health policy debate. The initial core projects will build upon embedded computational capabilities and data expertise, and will thus concentrate on the development of mathematical methodologies including: sparse state-space methods for the characterisation of disease progression in high-dimensional data using transition graphs in discrete spaces; time-varying networks and control for epidemics data; geometrical similarity graphs to link imaging and omics data for disease progression; stochastic processes and community detection from NHS patient data wedding behavioural and social network data with personal health indicators; statistical learning for the analysis of stratified medicine. The mathematical techniques used to address these requirements will need to combine, among others, ingredients from dynamical and stochastic systems with graph-theoretical notions, sparse statistical learning, inference and optimisation. The Centre will be led by Mathematics but researchers in the Centre span mathematical, biomedical, clinical and computational expertise.