Personalised Medicine through Learning in the Model Space

通过模型空间学习实现个性化医疗

• 批准号: EP/L000296/1
• 负责人: Peter Tino
• 金额: $ 132.96万
• 依托单位: University of Birmingham
• 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%2FL000296%2F1
• 关键词: Personalised; Medicine; through; Learning; Model

In order to achieve the goal of truly personalised healthcare and disease treatments tailored specifically for each individual patient, we should be able to understand why a disease appears or progresses, how does it happen, where it would happen and in how long this will happen. It is not an easy task.Mathematics is playing an ever-increasing role in the area of health and medicine, through the use of predictive modelling, statistics, and virtual simulations. Such mathematical tools are becoming invaluable in testing the feasibility of therapeutic procedures and medical devices prior to clinical trials. Furthermore, over the coming years computer models coupled to patient-specific diagnostics will be used in real time in the clinical environment to directly advise on treatment strategies. Given the wealth of (many times) disconnected biological, epidemiological and environmental information on a disease and adding on top of this the multiple paths that we as individuals can follow (a change in lifestyle, a geographical change, etc.) and our own individual characteristics (genes, anatomy, weight, age, etc.) it is not surprising that personalised models are difficult to achieve. There is data, information and knowledge that we must be able to connect via mathematical approaches in order to represent the mechanisms of the disease and the unique journey that we all follow. From a modeller's perspective, this is an incredible conundrum: what is important/ what is not? how do I formulate the cause-effect relationships with this disparate data if I don't understand how one risk factor or variable relates to another?The aim of this project is to be able to 'guide' the modeller from the data and to provide personalised models for diagnosis and treatment. Starting from an already existing (partial) explanation of the disease constructed in a mechanistic mathematical way (explanation-based or hypotheses driven), the information should lead the modeller. In order to do this in a systematic way, we propose that the information will be built into so-called "data-driven" models: i.e, models that fit the data but don't explain why. These "data-driven" models are "intelligent": they learn from the data and information that they have. If these "data-driven" models could learn in the same space that the mechanistic models try to explain, there is a possible path of common understanding of these two approaches that could potentially exist. And this is the path that we intend to explore and define.The different levels in personalised medicine that will be considered in this project are the following:- Cell & organ level: in the context of this project, with 'cell & organ level' we mean the behavior of individual cells (cell level), the joined behavior of all cells in a tissue (tissue level) and the combined behavior of the tissues in an organ (organ level).- patient level: with 'patient level' we mean the properties and processes of organs and patients, part of which can be observed through online monitoring, visual inspection, therapy records, etc.- care level: with care level we mean the whole of actions of nurses and doctors, the behavior of the support systems, the applicable guidelines and policies, etc. which are external to the patient but have a significant impact on his condition.The developed methods will allow one to perform the following prediction and inference tasks:- Assessment of risk of a range of potential complications.- Early warning for and diagnosis of such conditions.- Simulation of effects of possible treatments for individual patients.

为了实现真正针对每个患者量身定制的个性化医疗保健和疾病治疗的目标，我们应该能够理解疾病为何出现或进展、如何发生、在哪里发生以及会发生多长时间。这不是一件容易的事。通过预测建模、统计和虚拟模拟的使用，数学在健康和医学领域发挥着越来越大的作用。这些数学工具对于在临床试验之前测试治疗程序和医疗设备的可行性变得非常有价值。此外，未来几年，与患者特定诊断相结合的计算机模型将在临床环境中实时使用，以直接建议治疗策略。鉴于有关疾病的大量（很多时候）互不相关的生物学、流行病学和环境信息，再加上我们作为个体可以遵循的多种路径（生活方式的改变、地理变化等）以及我们自己的个体特征（基因、解剖学、体重、年龄等），因此个性化模型难以实现也就不足为奇了。我们必须能够通过数学方法将数据、信息和知识联系起来，以便代表疾病的机制和我们所有人都遵循的独特旅程。从建模者的角度来看，这是一个令人难以置信的难题：什么重要/什么不重要？如果我不了解一个风险因素或变量与另一个风险因素或变量之间的关系，如何用这些不同的数据制定因果关系？该项目的目的是能够根据数据“指导”建模者并提供用于诊断和治疗的个性化模型。从以机械数学方式（基于解释或假设驱动）构建的疾病的现有（部分）解释开始，信息应该引导建模者。为了以系统的方式做到这一点，我们建议将信息构建到所谓的“数据驱动”模型中：即适合数据但不解释原因的模型。这些“数据驱动”模型是“智能的”：它们从拥有的数据和信息中学习。如果这些“数据驱动”模型能够在机械模型试图解释的同一空间中学习，那么就可能存在对这两种方法达成共识的可能路径。这就是我们打算探索和定义的路径。本项目将考虑的个性化医疗的不同级别如下： - 细胞和器官级别：在本项目中，“细胞和器官级别”是指单个细胞的行为（细胞级别）、组织中所有细胞的联合行为（组织级别）以及器官中组织的组合行为（器官级别）。 - 患者级别：“患者级别”是指细胞的特性和过程。 器官和患者，其中部分可以通过在线监测、目视检查、治疗记录等进行观察。 - 护理级别：护理级别是指护士和医生的整体行为、支持系统的行为、适用的指南和政策等，这些对患者来说是外部的，但对患者的病情有重大影响。所开发的方法将允许人们执行以下预测和推理任务： - 评估一系列潜在并发症的风险。 - 早期预警 以及此类病症的诊断。- 模拟个别患者可能的治疗效果。

项目成果

Structural Identifiability and Indistinguishability analyses of Cardiovascular Feedback Models

心血管反馈模型的结构可识别性和不可区分性分析

• DOI: 10.1016/j.ifacol.2015.10.131
• 发表时间: 2015
• 期刊: IFAC-PapersOnLine
• 作者: Tariq Abdulla;N. Evans;J. Yates;T. Collins;Jerome T. Mettetal;M. Chappell
• 通讯作者: M. Chappell

A multiscale modelling approach to understand atherosclerosis formation: A patient-specific case study in the aortic bifurcation.

• DOI: 10.1177/0954411917697356
• 发表时间: 2017-05
• 期刊: Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine
• 作者: Alimohammadi M;Pichardo-Almarza C;Agu O;Díaz-Zuccarini V
• 通讯作者: Díaz-Zuccarini V

Aortic dissection simulation models for clinical support: fluid-structure interaction vs. rigid wall models.

• DOI: 10.1186/s12938-015-0032-6
• 发表时间: 2015-04-15
• 期刊: Biomedical engineering online
• 影响因子: 3.9
• 作者: Alimohammadi M;Sherwood JM;Karimpour M;Agu O;Balabani S;Díaz-Zuccarini V
• 通讯作者: Díaz-Zuccarini V

Development of a Patient-Specific Multi-Scale Model to Understand Atherosclerosis and Calcification Locations: Comparison with In vivo Data in an Aortic Dissection.

• DOI: 10.3389/fphys.2016.00238
• 发表时间: 2016
• 期刊: Frontiers in physiology
• 影响因子: 4
• 作者: Alimohammadi M;Pichardo-Almarza C;Agu O;Díaz-Zuccarini V
• 通讯作者: Díaz-Zuccarini V

Kernel regression estimates of time delays between gravitationally lensed fluxes

• DOI: 10.1093/mnras/stw510
• 发表时间: 2015-08
• 期刊: Monthly Notices of the Royal Astronomical Society
• 影响因子: 4.8
• 作者: Sultanah Al Otaibi;Peter Tivno;J. Cuevas-Tello;I. Mandel;Somak Raychaudhury