Predicting and monitoring cardiovascular outcomes using wearable devices and novel machine learning techniques

使用可穿戴设备和新颖的机器学习技术预测和监测心血管结果

• 批准号: 2720272
• 金额: --
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2720272
• 关键词: Predicting; monitoring; cardiovascular; outcomes; using

1) Brief description of the context of the research including potential impactDiagnosis and monitoring of cardiovascular disease(CVD) requires intrusive, in-clinic testing. This requires patients to take time out of their daily lives, as well as clinical resources to perform tests, and only reflects a small snapshot of time. Wearable devices offer a solution to this problem - patients can wear them freely in their daily lives, whilst having relevant clinical data collected regularly or even continuously.Wearables have been massively adopted by consumers, with over 1 billion connected devices as of 2022 [1]. This adoption shows that people are comfortable with electronics that monitor their health, and many products such as the Apple Watch use health awareness as a large part of their product offering through heart rate monitoring, SpO2 max., and other fitness measurements.By combining this wealth of data with novel time series methods borne out of advancements in machine learning/artificial intelligence, wearables can help move cardiovascular monitoring from the clinic to the home, as well as help at-risk users seek medical attention that could stop or slow the progression of their conditions. The potential impact for this technology is huge - cardiovascular disease is responsible for 25% of deaths in the UK, and clinical resources are already stretched. Moving the burden of diagnosis from the clinic to an algorithm, or simply offering clinicians another metric for prioritising patient care, could have a massive effect on patient wellbeing. 2) Aims and ObjectivesThe key aim of this research is to investigate whether advanced ML/AI based time series analysis techniques can predict cardiac outcomes using data collected from wearable devices. 3) Novelty of Research MethodologyThis research aims to use previously unused features in wearable signals (e.g. photoplethysmography) to predict cardiac outcomes, as well as applying new methods to known signals to uncover or improve their predictive power, making them more useful and trusted in a clinical environment.4) Alignment to EPSRC's strategies and research areasHealthcare technologies, artificial intelligence5) Any companies or collaborators involvedNo[1] Statista. "Global connected wearable devices 2016-2022". Available at: https://www.statista.com/statistics/487291/global-connected-wearable-devices/. Accessed [8 February 2022]

1)简要描述研究背景，包括潜在影响心血管疾病（CVD）的诊断和监测需要侵入性的临床试验。这需要患者从日常生活中抽出时间，以及临床资源来进行测试，并且只反映了时间的一小部分。可穿戴设备为这一问题提供了解决方案，患者可以在日常生活中自由佩戴，同时定期甚至连续收集相关临床数据。可穿戴设备已被消费者广泛采用，截至2022年，连接设备已超过10亿台。这种采用表明人们对监测健康的电子产品感到满意，许多产品（如Apple Watch）通过心率监测（SpO2 max）将健康意识作为其产品的很大一部分。，以及其他健康测量。通过将这些丰富的数据与机器学习/人工智能的进步所产生的新颖时间序列方法相结合，可穿戴设备可以帮助将心血管监测从诊所转移到家庭，并帮助有风险的用户寻求可能阻止或减缓其病情进展的医疗护理。这项技术的潜在影响是巨大的——英国25%的死亡是由心血管疾病造成的，而临床资源已经捉襟见肘。将诊断的负担从诊所转移到算法上，或者只是为临床医生提供另一种优先考虑患者护理的指标，可能会对患者的健康产生巨大影响。2)目的和目标本研究的主要目的是研究先进的基于ML/AI的时间序列分析技术是否可以利用从可穿戴设备收集的数据预测心脏预后。3)研究方法的新颖性本研究旨在利用可穿戴信号中以前未使用的功能（例如光容积脉搏波）来预测心脏结果，并将新方法应用于已知信号，以发现或提高其预测能力，使其在临床环境中更有用和可信。4)符合EPSRC的战略和研究领域：医疗保健技术、人工智能。5)任何公司或合作者参与。“2016-2022年全球互联可穿戴设备”。可在：https://www.statista.com/statistics/487291/global-connected-wearable-devices/。访问[2022年2月8日]