Personalised Medicine through Learning in the Model Space

为了实现专门为每个患者量身定做的真正个性化医疗保健和疾病治疗的目标，我们应该能够理解一种疾病为什么会出现或进展，它是如何发生的，它将在哪里发生，以及这种情况将在多长时间内发生。这并不是一项容易的任务。通过使用预测模型、统计学和虚拟模拟，数学在健康和医学领域发挥着越来越大的作用。这样的数学工具在临床试验前测试治疗程序和医疗设备的可行性方面正变得非常宝贵。此外，在未来几年，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，以直接为治疗策略提供建议。考虑到关于一种疾病的(多次)互不相关的生物学、流行病学和环境信息的丰富，再加上我们作为个人可以遵循的多种途径(生活方式的改变，地理上的改变，等等)。以及我们自己的个体特征(基因、解剖结构、体重、年龄等)。个性化模式很难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，以代表疾病的机制和我们所有人所经历的独特旅程。从建模师的角度来看，这是一个令人难以置信的难题：什么是重要的，什么不重要？如果我不理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，我如何用这些完全不同的数据来表达因果关系？这个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的模型。从已经存在的(部分)以机械的数学方式(基于解释或假设驱动)构建的疾病解释开始，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：即符合数据但没有解释原因的模型。这些“数据驱动”的模型是“智能的”：它们从自己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，那么可能存在一条关于这两种方法的共同理解的路径。这就是我们打算探索和定义的路径。该项目中将考虑的个性化医学的不同级别如下：-细胞和器官级别：在该项目的上下文中，我们指的是单个细胞的行为(细胞级别)、组织中所有细胞的结合行为(组织级别)和器官中组织的组合行为(器官级别)。-患者级别：患者级别：我们指的是器官和患者的属性和过程，其中的一部分可以通过在线监测、视觉检查、治疗记录来观察，护理水平：我们所说的护理水平是指护士和医生的全部行动、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，但对患者的情况有重大影响。开发的方法将使人们能够执行以下预测和推断任务：-评估一系列潜在并发症的风险。-对这种情况的早期预警和诊断。-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.