Forecasting personal health in an uncertain environment

医疗服务是在嘈杂和不确定的环境中进行的。有些患者在出现第一个症状后就出现了，而另一些患者可能会等待，然后再去看医疗保健提供者。一些患者依从治疗并对治疗有反应，而另一些患者则没有。因此，如果不粗略地使用随机数来确定风险，预测个人的健康状况是困难的。另一方面，人体生理学的机械模型显示出作为研究工具的前景，但难以有效地应用于医疗保健问题，因为没有框架来嵌入在个体和人群中看到的结构和功能的变异性。这一大胆而雄心勃勃的提案的挑战是通过开发新的数学工具，在各个层面嵌入不确定性，使描述个人通过医疗保健的轨迹的模型能够通过器官系统的详细机械和多尺度模型获得信息，从而在医疗保健建模方面实现一步变化。该项目的独特之处在于它将生理学的机械模型与个性化医疗的建模相结合，并在所有尺度上整合了不确定性。我们认识到我们的愿景是大胆而雄心勃勃的。作为解决这个问题的一种“方法”，我们选择了两个具体的范例，其中项目合作伙伴已经拥有专业知识并可以访问模型、工具和数据。其中第一个是流感在人群中的传播，第二个是普遍的心律失常（心房颤动）。我们将采用“中间出”的方法，并将从患者（个体）规模开始，每个样本都有丰富的数据。从这一点出发，我们将向上研究群体尺度，向下研究分子尺度。传统的流感传播建模方法将考虑个体之间的接触，以及感染从一个人传播到另一个人的可能性。我们的方法将是剔除这种概率，代之以基于个人健康状况（他们是否吸烟，是否患有哮喘等）的感染可能性模型，再加上群体变异性（例如气道几何形状，这是吸入颗粒如何转运到肺气道粘液层的关键决定因素）。对心脏中的心房颤动进行建模的传统方法将考虑与触发器组合的人类心房的几何形状。心房结构和功能的重塑将强加于模型。我们的方法是使用纵向研究的数据来参数化房颤模型，并使用模型的输出来确定对该患者最有益的治疗。在医疗保健系统面临的众多挑战中，由不完整信息提供信息的临床决策可能导致治疗成功率低、资源利用效率低和患者预后差。该提案旨在使临床医生能够更好地利用患者特定的数据，并通过机械建模提供信息。我们寻求将多尺度生理建模推向一个新的复杂水平，通过在不同的组织尺度上系统地整合不确定性和可变性，并将结果转化为临床结果。我们的目标是让我们的方法在整个医疗保健行业中发挥作用：临床医生将能够对每位患者的结果做出更明智的预测;政策决策将由人口模型提供信息;制药公司将能够评估药物对个人的疗效;患者将享受更好的结果。

Healthcare delivery takes place in a noisy and uncertain environment. Some patients present following their first symptom, whereas others may wait before presenting to a healthcare provider. Some patients comply with and respond to treatment, while others don't. Forecasting the health status of an individual is consequently difficult without the crude use of random numbers to determine risk. On the other hand, mechanistic models of human physiology show promise as research tools, but are difficult to apply effectively to healthcare problems because there is no framework to embed the variability in structure and function that is seen in individuals and human population. The challenge of this bold and ambitious proposal is to bring about a step change in both modelling for healthcare by developing new mathematical tools that embed uncertainty at every level, enabling models that describe the trajectory of an individual through healthcare to be informed by detailed mechanistic and multiscale models of organ systems. This project is unique in that it combines mechanistic models of physiology with the modelling personalised medicine with the integration of uncertainty at all scales.We recognise that our vision is bold and ambitious. As a "way in" to this problem we have selected two specific exemplars where the project partners already have expertise and access to models, tools, and data. The first of these is the transmission of influenza within a population, and the second is a prevalent cardiac arrhythmia (atrial fibrillation). We will adopt a "middle out" approach, and will start at the patient (individual) scale where there are rich data for each exemplar. From this point we will work upwards to population scale, and downwards to the molecular scale. A conventional approach to model transmission of influenza would take into account contact between individuals, with a probability of infection passing from one to another. Our approach will be to unpick this probability, replacing it with a model of the likelihood of infection based on knowledge of the health status of an individual (do they smoke, do they have asthma, etc.), coupled with population variability (for example of airway geometry, a key determinant of how inhaled particles are transported to the mucus layer of lung airways). A conventional approach to modelling atrial fibrillation in the heart would take into account the geometry of the human atrium, combined with a trigger. Remodelling of the structure and function of the atria would be imposed on the model. Our approach will be to parameterise a model of atrial fibrillation using data from longitudinal studies, and use output from the model to determine the treatment that would have been most beneficial for that patient. Among the many challenges faced by the healthcare system, clinical decisions informed by incomplete information can lead to low treatment success rates, inefficient use of resources and poor patient outcomes. This proposal seeks to enable clinicians to make better use of patient-specific data, informed by mechanistic modelling. We seek to push multiscale physiological modelling to a new level of sophistication, by incorporating uncertainty and variability systematically across different scales of organization and translating the outcomes to the clinic. Our ambition is for our approach to find utility across the healthcare sector: the clinician will be able to make better-informed forecast of outcomes for each patient; policy decisions will be informed by population models; pharma companies will be able to assess the efficacy of drugs on individuals; and the patient will enjoy improved outcomes.