EPSRC Hub for Quantitative Modelling in Healthcare

我们的中心将数学家、统计学家和临床医生团队与一系列行业合作伙伴、患者和其他利益相关者聚集在一起，专注于开发新的量化方法，应用于诊断和管理长期健康状况，如糖尿病和精神病，并抗击抗菌素感染，如败血症和支气管扩张。这一方法的基础是集中在埃克塞特大学的世界领先的糖尿病、微生物群落、医学真菌学和精神健康方面的专业知识。它利用Hub团队广泛的理论和方法专业知识，在研究和翻译方面提供创新的方法。虽然定量建模是在经济和金融领域使用的一种成熟的工具，但尖端的定量分析直到最近才在卫生保健中成为可能。然而，到目前为止，它一直局限于医疗服务和系统管理背景下的医疗经济学。开发未来疗法、优化治疗和改善社区健康和护理的应用还处于初级阶段。这是由于从数学(方法论)以及临床和患者的角度提出的一些挑战。我们的中心方法将使我们能够开发与我们的临床和工业合作伙伴相关的、由相关患者群体提供信息的新的统计和数学方法。建立这一新一代量化模型需要我们提高对健康和疾病的有效网络相互作用和紧急模式的数学理解。数学和统计学进展的临床转化要求我们进一步发展稳健的不确定性量化方法，用于新的治疗、治疗或干预预测和评估。NHS的长期规划渴望提供更加个性化和以患者为中心的医疗保健，更加注重预防，并且更有可能在社区和医院外提供。我们的中心将通过开发所需的数学和统计工具来为患者的临床决策提供信息，从而为此做出贡献。这种方法的基础是定量的特定于患者的数学模型，其参数直接根据患者的个体数据确定。作为一个例子，我们最近在精神健康领域的研究表明，运动特征可以用来区分健康受试者和精神分裂症患者。这一假设在一组精神分裂症患者中进行了测试，我们开发了一种定量分析管道，允许将个人分类为健康人或患者。用于分类的特征涉及个体运动属性的数据驱动模型以及与虚拟伴侣的协调措施，被提出为社交恐惧症的新生物标记物。为了在NHS环境中验证这一点，我们最近与德文郡伙伴精神健康信托基金的精神病早期干预团队合作开展了一项可行性研究。这项研究的成功可以通过使用易于测量和分析的新标记物进行诊断，极大地促进精神病的早期发现，并提高诊断的准确性。事实上，个性化的定量模型有望改变一系列临床疾病的预后、诊断和治疗。例如，在存在一系列治疗选择的糖尿病患者中，根据个人情况确定最佳药物及其给药模式将使我们能够最大限度地提高疗效，同时将不想要的副作用降至最低。

Our Hub brings together a team of mathematicians, statisticians and clinicians with a range of industrial partners, patients and other stakeholders to focus on the development of new quantitative methods for applications to diagnosing and managing long-term health conditions such as diabetes and psychosis and combating antimicrobial infections such as sepsis and bronchiectasis. This approach is underpinned by the world-leading expertise in diabetes, microbial communities, medical mycology and mental health concentrated at the University of Exeter. It uses the breadth of theoretical and methodological expertise of the Hub's team to give innovative approaches to both research and translational aspects. Although quantitative modelling is a well-established tool used in the fields of economics and finance, cutting-edge quantitative analysis has only recently become possible in health care. However, up to now it has been restricted to health economics in the context of healthcare services and systems management. Applications to develop future therapies, optimising treatments and improving community health and care are in its infancy. This is due to a number of challenges from both mathematical (methodological) as well as clinical and patients' perspectives. Our Hub approach will allow us to develop novel statistical and mathematical methodologies of relevance to our clinical and industrial partners, informed by relevant patient groups. Building this new generation of quantitative models requires that we advance our mathematical understanding of the effective network interaction and emergent patterns of health and disease. Clinical translation of mathematical and statistical advances necessitates that we further develop robust uncertainty quantification methodology for novel therapy, treatment or intervention prediction and evaluation. NHS long-term planning aspires to deliver healthcare that is more personalised and patient centred, more focused on prevention, and more likely to be delivered in the community, out of hospital. Our Hub will contribute to this through developing mathematical and statistical tools needed to inform clinical decision making on a patient-by-patient basis. The basis of this approach is quantitative patient-specific mathematical models, the parameters of which are determined directly from individual patient's data. As an example of this, our recent research in the field of mental health has revealed that movement signatures could be used to distinguish between healthy subjects and patients with schizophrenia. This hypothesis was tested in a cohort of people with schizophrenia and we developed a quantitative analysis pipe line allowing for classification of individuals as healthy or patients. The features used for classification involving data-driven models of individual movement properties as well as measures of coordination with a virtual partner were proposed as a novel biomarker of social phobias. To validate this in an NHS setting, we have recently carried out a feasibility study in collaboration with the early intervention for psychosis teams in Devon Partnership Mental Health Trust. The success of this study could significantly advance the early detection of psychosis by enabling diagnosis using novel markers that are easily measured and analysed and improve accuracy of diagnosis. Indeed, personalised quantitative models hold the promise for transforming prognosis, diagnosis and treatment of a wide range of clinical conditions. For example, in diabetes where a range of treatment options exist, identifying the optimal medication, and the pattern of its delivery, based upon the profile of the individual will enable us to maximise efficacy, whilst minimising unwanted side effects.