Personalised Medicine through Learning in the Model Space

通过模型空间学习实现个性化医疗

基本信息

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

项目摘要

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.

为了实现专门为每个患者量身定制的真正个性化的医疗保健和疾病治疗的目标，我们应该能够理解疾病为什么出现或进展，如何发生，发生在哪里以及在这种情况下发生多长时间。这不是一件容易的事。通过使用预测建模，统计和虚拟模拟，数学在健康和医学领域中发挥了不断增强的作用。在临床试验之前，这种数学工具在测试治疗程序和医疗设备的可行性方面变得非常宝贵。此外，在未来几年中，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（很多时候）关于疾病的生物学，流行病学和环境信息的财富，并在其中增加了我们作为个人所能遵循的多种途径（生活方式的变化，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），这并不令人惊讶，因此很难实现个性化模型。有数据，信息和知识，我们必须能够通过数学方法连接，以代表疾病的机制以及我们所有人遵循的独特旅程。从建模者的角度来看，这是一个令人难以置信的难题：什么是重要/不是什么？如果我不了解一种风险因素或可变因素与另一个危险因素有何关系，该如何与此不同的数据建立因果关系的关系？该项目的目的是能够从数据中“指导”建模器并提供个性化的诊断和治疗模型。从以机械数学方式（基于解释或假设驱动的说明）构建的疾病的现有（部分）解释开始，该信息应引导建模器。为了以系统的方式执行此操作，我们建议将信息内置到所谓的“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”：它们从它们拥有的数据和信息中学习。如果这些“数据驱动”模型可以在机械模型试图解释的同一空间中学习，则可能会有一种可能存在的两种方法的共同理解的途径。这是我们打算探索和定义的途径。该项目中将考虑的个性化医学水平的不同水平如下： - 细胞和器官级：在该项目的背景下，“细胞和器官水平”，我们的意思是单个细胞的行为（细胞水平）（细胞水平）（组织水平）中所有细胞的行为（组织级别）（组织级别）和组织级别（器官级别的级别）（级别）的层面（我们的级别）。器官和患者，可以通过在线监控，视觉检查，治疗记录等观察到部分。和这种状况的诊断。-模拟对个别患者可能治疗的影响。

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

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
影响因子：
0
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.