Network: POEMS - Predictive mOdelling for hEalthcare technologies through MathS

网络：POEMS - 通过 MathS 进行医疗保健技术的预测建模

• 批准号: EP/L001101/1
• 负责人: Richard Clayton
• 金额: $ 32.41万
• 依托单位: University of Sheffield
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2013
• 资助国家: 英国
• 起止时间: 2013 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FL001101%2F1
• 关键词: Network; POEMS; Predictive; mOdelling; hEalthcare

The delivery of healthcare is an increasingly important policy question, given an ageing population, and the need to decrease operating costs. The mathematics, engineering, and physical sciences (EPS) communities have a potentially important role to play in developing new technologies that will enable healthcare to be delivered in a more effective and personalised way. The key to maximising the translation of tools and ideas from the academic research community to clinical practice is engagement and interaction amongst a diverse and multidisciplinary community. This network has arisen from an EPSRC sandpit on Predictive Modelling for Healthcare Technologies Through Maths, and the initial membership of the network will be the sandpit participants. However, a key aim of the network is to grow, connect, and co-ordinate the UK research community working predictive models, well beyond the pool of participants who attended the sandpit, and especially focusing on areas that were under-represented amongst the sandpit attendees. In particular, we will target new members from the clinical, healthcare, mathematics, experimental biology, and industrial communities, with research interests aligned with the network objectives. The proposed size of the network is 200 members.The overall aim of the network is to be an accelerating mechanism for collaborations and research activity on predictive modelling for healthcare. These in turn will form the building blocks of a wider vision and research agenda on a national scale beyond the lifetime of the network. These goals will be achieved through four main types of activity: network assemblies, themed events, travel grants, and continuous communication within the network using social media.Network assemblies bracket the overall activity, occuring in the first and last years of the network period. The first network assembly will have the specific the aim of identifying research challenges and initial collaborative groupings. The final network assembly will allow a review of progress made in our overarching research agenda, and be an opportunity to create a road map for the next 10 years of research in predictive modelling for healthcare technologies.Throughout the period, the network will provide the infrastructure and resources to enable this feltwork of collaborations to be extended to new partners, developed and nurtured to the level where they can deliver publications and funding proposals. Themed events will be proposed by subgroups of the membership, and will focus on particular topics within the research agenda. This allows for an organic bottom-up mechanism by which promising lines of future research will be identified by members of the community. This approach will ensure that all expertise and creativity contained in the UK research base is exploited to the full when shaping the national research agenda in modelling healthcare for the years 2013-2016 and beyond. Travel grants will allow members to invite prestigious visitors to the UK to explore new collaborations. Communication between the network membership will be continuous: we aim to have an active online community, as well as facilitating face-to-face events.Communication of the value and potential of predictive models in healthcare to academia, end-users and the general public through outreach activities is one of the network objectives. This will be achieved by bidding to organise sessions at national conferences, by outreach to the general public through events such as the British Science Festival, and through engagement with patient groups.

鉴于人口老龄化和降低运营成本的需要，提供医疗保健是一个日益重要的政策问题。数学、工程和物理科学(EPS)社区在开发新技术方面发挥着潜在的重要作用，这些技术将使医疗保健以更有效和个性化的方式提供。最大限度地将工具和想法从学术研究社区转化为临床实践的关键是不同和多学科社区之间的参与和互动。这个网络是从EPSRC关于通过数学为医疗保健技术进行预测建模的沙坑中产生的，网络的初始成员将是沙坑参与者。然而，该网络的一个关键目标是发展、连接和协调英国研究社区使用预测模型，远远超出参加沙坑的参与者的范围，特别是关注沙坑参与者中代表性不足的领域。特别是，我们将瞄准来自临床、医疗保健、数学、实验生物学和工业社区的新成员，其研究兴趣与网络目标保持一致。该网络的拟议规模为200名成员。该网络的总体目标是为医疗保健预测建模方面的合作和研究活动提供一个加速机制。这些反过来又将构成更广泛的愿景和全国范围内研究议程的基石，超越网络的生命周期。这些目标将通过四种主要类型的活动来实现：网络集会、主题活动、旅行补助金和利用社交媒体在网络内持续交流。网络集会包括在网络期间的第一年和最后几年进行的总体活动。第一次网络会议将具有确定研究挑战和初步合作分组的具体目标。最终的网络大会将使我们能够审查我们总体研究议程中取得的进展，并成为一个机会，为医疗保健技术预测建模未来10年的研究制定路线图。在此期间，网络将提供基础设施和资源，使这项协作工作能够扩展到新的合作伙伴，发展和培育到他们可以提供出版物和资金建议的水平。主题活动将由成员小组提出，并将重点放在研究议程中的特定主题上。这允许一个有机的自下而上的机制，社区成员将通过该机制确定未来研究的有希望的路线。这一方法将确保在制定2013-2016年及以后国家医疗保健建模研究议程时，充分利用英国研究基础中包含的所有专业知识和创造力。旅行补助将允许会员邀请知名游客到英国探索新的合作。网络成员之间的交流将是持续的：我们的目标是拥有一个活跃的在线社区，并促进面对面的活动。通过外联活动向学术界、最终用户和普通公众传播预测模型在医疗保健中的价值和潜力是网络目标之一。这将通过竞标在国家会议上组织会议、通过英国科学节等活动向普通公众宣传以及通过与患者群体接触来实现。

项目成果

Modelling pulsatile blood flow through surgically coupled microvascular anastomoses

通过手术耦合微血管吻合模拟脉动血流

• DOI: 10.48550/arxiv.1804.04984
• 发表时间: 2018
• 作者: Gallagher M

Finite Element Predictions of Sutured and Coupled Microarterial Anastomoses

缝合和耦合微动脉吻合术的有限元预测

• DOI: 10.14326/abe.8.63
• 发表时间: 2019
• 期刊: Advanced Biomedical Engineering
• 影响因子: 1
• 作者: Wain R

Patient-Specific, Multi-Scale Modeling of Neointimal Hyperplasia in Vein Grafts.

• DOI: 10.3389/fphys.2017.00226
• 发表时间: 2017
• 期刊: Frontiers in physiology
• 影响因子: 4
• 作者: Donadoni F;Pichardo-Almarza C;Bartlett M;Dardik A;Homer-Vanniasinkam S;Díaz-Zuccarini V
• 通讯作者: Díaz-Zuccarini V

Defining vitamin D status using multi-metabolite mathematical modelling: A pregnancy perspective.

• DOI: 10.1016/j.jsbmb.2019.03.024
• 发表时间: 2019-06
• 期刊: The Journal of steroid biochemistry and molecular biology
• 作者: Beentjes CHL;Taylor-King JP;Bayani A;Davis CN;Dunster JL;Jabbari S;Mirams GR;Jenkinson C;Kilby MD;Hewison M;Tamblyn JA
• 通讯作者: Tamblyn JA

Influence of microvascular sutures on shear strain rate in realistic pulsatile flow.

• DOI: 10.1016/j.mvr.2018.03.001
• 发表时间: 2018-07
• 期刊: Microvascular research
• 影响因子: 3.1
• 作者: R. Wain;David J. Smith;Doug E. Hammond;J. Whitty