ASPIRE: Automated Sensing & Predictive Inference for Respiratory Exacerbation

ASPIRE：自动传感

• 批准号: EP/P009824/1
• 负责人: David Clifton
• 金额: $ 188.01万
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FP009824%2F1
• 关键词: ASPIRE; Automated; Sensing; Predictive; Inference

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.

人们迫切需要可靠的智能系统，该系统可以在家中监测患者的病情，并帮助患者管理长期病情，但这一需求尚未得到满足。延迟认识生理状态的变化会导致结果恶化并增加医疗费用。 ASPIRE 计划以慢性阻塞性肺疾病 (COPD) 为例，该疾病影响着全球超过 2.1 亿人。国家卫生服务中心每年花费超过 8 亿英镑来治疗这种疾病，其中一半以上用于在医院治疗患者，而不是在家中照顾患者。需要智能监测系统来满足在家中长期患有这种疾病的患者的需求。然而，由于以下原因，可穿戴系统尚未大规模渗透到临床实践中：（i）现有可穿戴设备对监测的耐受性较差； (ii) 可穿戴传感器产生的生命体征估计缺乏稳健性； (iii) 电池寿命非常有限，需要以一定的速率对电池进行充电，从而妨碍其大规模使用； (iv) 限制随后使用数据来帮助患者了解和管理其病情。我们建议开发一种“智能”家庭系统，将智能算法嵌入轻型医疗保健传感器中，以克服这些限制。我们的新颖工作将结合下一代机器学习算法，将来自医疗保健传感器的信息与来自全科医生和医院就诊的信息结合起来。这将使系统能够了解个体患者的“正常”健康状况，了解他们可能患有的其他状况，然后可以向患者提出有关其状况自我管理的建议。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于个体患者来说是正确的。

项目成果

Breathing Rate Estimation From the Electrocardiogram and Photoplethysmogram: A Review.

• DOI: 10.1109/rbme.2017.2763681
• 发表时间: 2018
• 期刊: IEEE reviews in biomedical engineering
• 影响因子: 17.6
• 作者: Charlton PH;Birrenkott DA;Bonnici T;Pimentel MAF;Johnson AEW;Alastruey J;Tarassenko L;Watkinson PJ;Beale R;Clifton DA
• 通讯作者: Clifton DA

Continuous Patient State Attention Models

连续患者状态注意力模型

• DOI: 10.1101/2022.12.23.22283908
• 发表时间: 2022
• 作者: Chauhan V

Mixture of Input-Output Hidden Markov Models for Heterogeneous Disease Progression Modeling

用于异质疾病进展建模的输入输出混合隐马尔可夫模型

• DOI: 10.1109/bhi56158.2022.9926903
• 发表时间: 2022
• 作者: Ceritli T