Title: Personalised Medicine through Learning in the Model Space
Funder: Engineering and Physical Sciences Research Council

Abstract: 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.


Research activity codes: 8.4 RESEARCH DESIGN AND METHODOLOGIES (HEALTH SERVICES);2.5 RESEARCH DESIGN AND METHODOLOGIES (AETIOLOGY)
Health categories: GENERIC HEALTH RELEVANCE