A combined modelling and machine learning approach to simulate vital signs in critically-ill patients.

EPSRC接受的相关研究领域有：数学生物学、非线性系统、人工智能技术。该项目将成为epsrc资助的CHIMERA医疗保健中心数学科学的一部分。CHIMERA旨在开发新的数学模型和数据科学工具，利用为每位患者收集的丰富的生理数据，为临床医生提供更好的信息，了解患者进入ICU后的身体恢复情况。该项目将探索模拟呼吸系统和心血管系统之间的机械反应和相互作用的各种竞争者模型，并结合分析，数值和先进的机器学习技术来优化模型结构并将参数拟合到可用的临床数据中。对于表现最好的模型，通过改变临床相关参数的值来产生大量的虚拟受试者，目的是将患者划分为某一事件的不同风险类别。考虑到来自模型参数的分类决策边界可能是非线性的，并且不是单连通的，将为每个候选模型实现深度学习架构。我们将明确咨询我们的临床合作伙伴，以了解获得的风险分类如何/哪种风险分类最有助于临床决策。

The relevant EPSRC accepted research areas are:Mathematical BiologyNonlinear SystemsArtificial Intelligence Technologies.This project will be part of the EPSRC-funded CHIMERA mathematical science in healthcare hub. CHIMERA aims to develop new mathematical models and data science tools which utilise the wealth of physiological data collected for each patient to provide clinicians with a better idea of how well the patient's body is recovering since their admission to ICU. This project will explore various contender models that simulate the mechanical responses and interactions between respiratory and cardiovascular systems and use a combination of analytical, numerical and advanced machine learning techniques to optimise the model structure and fit the parameters to the available clinical data. For the best performing models, large cohorts of virtual subjects will be produced by varying the values of clinically relevant parameters with the purpose of classifying patients into different risk categories of a certain event. Deep-learning architectures will be implemented for each candidate model, given that the classification decision boundaries from model parameters are likely to be non-linear and not simply connected. Our clinical partners will be explicitly consulted to see how/which of the risk classifications obtained might best assist clinical decision making.