Personalised Medicine through Learning in the Model Space

为了实现真正个性化的医疗保健和针对每个患者量身定制的疾病治疗目标，我们应该能够了解疾病出现或发展的原因、发生的方式、发生的位置以及发生的时间。这不是一件容易的事。通过使用预测建模、统计和虚拟模拟，数学在卫生和医学领域发挥着越来越大的作用。在临床试验之前，这些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，计算机模型与患者特异性诊断相结合，将在临床环境中实时使用，直接提供治疗策略建议。考虑到一种疾病的大量（很多时候）不相关的生物、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途径（生活方式的改变、地理位置的变化等）和我们自己的个体特征（基因、解剖结构、体重、年龄等），个性化模型很难实现也就不足为奇了。有数据、信息和知识，我们必须能够通过数学方法联系起来，以表示疾病的机制和我们都遵循的独特旅程。从建模者的角度来看，这是一个难以置信的难题：什么是重要的/什么是不重要的？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，我如何用这些不同的数据来表述因果关系？该项目的目的是能够从数据中“指导”建模者，并为诊断和治疗提供个性化模型。从以机械数学方式（基于解释或假设驱动）构建的已经存在的（部分）疾病解释开始，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”的模型是“智能的”：它们从所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相同空间中学习，那么就有可能存在对这两种方法的共同理解的可能路径。这就是我们想要探索和定义的道路。本项目将考虑的个体化医疗的不同水平如下：-细胞和器官水平：在本项目的上下文中，“细胞和器官水平”是指单个细胞的行为（细胞水平），组织中所有细胞的联合行为（组织水平）和器官中组织的综合行为（器官水平）。-患者层面：“患者层面”指的是器官和患者的特性和过程，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。-护理水平：护理水平指的是护士和医生的整体行动、支持系统的行为、适用的指导方针和政策等，这些都是患者外部的，但对其病情有重大影响。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。-对这些情况进行早期预警和诊断。-对个别患者可能的治疗效果进行模拟。

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.