ASPIRE: Automated Sensing & Predictive Inference for Respiratory Exacerbation

人们迫切需要可靠的智能系统来监测家中的患者状况，并帮助患者管理长期状况。延迟识别生理状态的变化会恶化结果并增加医疗保健成本。ASPIRE方案以慢性阻塞性肺疾病（COPD）为例，这种疾病影响全球2.1亿多人。英国国家医疗服务体系每年为此花费超过8亿英镑，其中一半以上用于在医院治疗患者，而不是在家中照顾他们。需要智能监控系统来满足长期在家的患者的需求。然而，由于以下原因，还没有可穿戴系统大规模地渗透到临床实践中：（i）现有可穿戴设备对监测的耐受性差;（ii）可穿戴传感器产生的生命体征的估计缺乏鲁棒性;（iii）非常有限的电池寿命，其需要以阻止其大规模使用的速率对电池进行再充电;和（iv）有限的后续使用的数据，以帮助病人了解和管理他们的病情。我们建议开发一个“智能”的家庭为基础的系统，智能算法嵌入在轻量级的医疗传感器，以克服这些限制。我们的新工作将结合下一代机器学习算法，将来自医疗传感器的联合收割机信息与来自GP和医院访问的信息结合起来。这将使系统能够学习个体患者的“正常”健康状况，了解他们可能遭受的其他状况，并且然后可以向患者提出关于他们状况的自我管理的建议。这项工作将包括与世界领先的临床医生密切合作，以确保系统提供的建议对个体患者是正确的。

There is an urgent, unmet need for reliable, intelligent systems that can monitor patient condition in the home, and which can help patients manage long-term conditions. Delays in recognition of the changes in physiological state worsen outcomes and increase healthcare costs. The ASPIRE programme uses chronic obstructive pulmonary disorder (COPD) as an exemplar, which affects over 210 million people globally. This condition costs the National Health Service over £800 million each year, over half of which is spent treating patients in hospital, rather than caring for them in their homes.Intelligent monitoring systems are required to address the needs of patients with long-term conditions in their homes. However, no wearable systems have penetrated into clinical practice at scale, due to: (i) poor tolerance of existing wearable devices for monitoring; (ii) a lack of robustness in the estimates of the vital signs that wearable sensors produce; (iii) very limited battery life that requires batteries to be re-charged at a rate that prevents their use on a large scale; and (iv) limited subsequent use of the data for helping the patient understand and manage their condition.We propose to develop an "intelligent" home-based system, with smart algorithms embedded within lightweight healthcare sensors, to overcome these limitations. Our novel work will incorporate next-generation machine learning algorithms to combine information from healthcare sensors with information from GP and hospital visits. This will enable the system to learn "normal" health condition for individual patients, with knowledge of other conditions from which they may be suffering, and which can then make recommendations to the patient concerning self-management of their condition. This work will include close working with world-leading clinicians to ensure that the recommendations provided by the system are correct for the individual patient.