EPSRC Hub for Quantitative Modelling in Healthcare

我们的中心汇集了一个数学家，统计学家和临床医生团队，以及一系列工业合作伙伴，患者和其他利益相关者，专注于开发新的定量方法，用于诊断和管理长期健康状况，如糖尿病和精神病，以及对抗败血症和支气管扩张等抗菌素感染。这种方法得到了埃克塞特大学在糖尿病、微生物群落、医学真菌学和精神健康方面世界领先的专业知识的支持。它利用中心团队的理论和方法专业知识的广度，为研究和翻译方面提供创新方法。虽然定量建模是经济学和金融学领域的一个成熟工具，但尖端的定量分析直到最近才在医疗保健领域成为可能。然而，到目前为止，它已被限制在医疗保健服务和系统管理的背景下的卫生经济学。开发未来疗法、优化治疗和改善社区健康和护理的应用尚处于起步阶段。这是由于来自数学（方法学）以及临床和患者观点的许多挑战。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新型统计和数学方法，并由相关患者群体提供信息。建立新一代的定量模型需要我们推进对有效网络相互作用和健康与疾病的紧急模式的数学理解。数学和统计学进展的临床转化需要我们进一步开发用于新疗法、治疗或干预预测和评估的稳健的不确定性量化方法。NHS的长期规划旨在提供更加个性化和以患者为中心的医疗保健，更注重预防，更有可能在社区提供，而不是医院。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做出贡献。这种方法的基础是定量的患者特定的数学模型，其参数直接从个体患者的数据确定。作为一个例子，我们最近在精神健康领域的研究表明，运动签名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，可以将个体分类为健康或患者。用于分类的特征涉及个人运动特性的数据驱动模型以及与虚拟伙伴的协调措施，被提出作为社交恐惧症的新生物标志物。为了在NHS环境中验证这一点，我们最近与德文郡合作精神健康信托基金的精神病早期干预团队合作进行了一项可行性研究。这项研究的成功可以通过使用易于测量和分析的新型标记物进行诊断，并提高诊断的准确性，从而大大促进精神病的早期检测。事实上，个性化定量模型有望改变各种临床疾病的预后、诊断和治疗。例如，在存在一系列治疗选择的糖尿病中，根据个体的情况确定最佳药物及其输送模式将使我们能够最大限度地提高疗效，同时最大限度地减少不必要的副作用。

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.