Remote assessment of Mental Health Patients for Cardiac Safety using Smartwatch ECG

使用智能手表心电图对心理健康患者的心脏安全进行远程评估

• 批准号: 10043052
• 金额: $ 6.34万
• 依托单位: PULSEAI LTD
• 依托单位国家: 英国
• 项目类别: Grant for R&D
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=10043052
• 关键词: Remote; assessment; Mental; Health; Patients

This machine learning project uses deep neural networks to investigate historical ECG databases containing more than 1 million patients to develop new methods of assessing cardiac risk in mental health patients from smartwatch ECG data.It aims to establish new ECG features that improve the prediction of arrhythmias due to drug interactions and builds on the company's established AI-enabled ECG interpretation software which has demonstrated Cardiologist level performance for arrhythmia detection.We use AI to extract subtle changes from smartwatch ECG signals which indicate the patient is experiencing an adverse reaction to their medication, for which they need immediate treatment. This enables us to create a cloud-based analytics platform where patients can be more effectively managed remotely.We all live in a hectic world where seeing the doctor and getting checkups are more complicated. This, coupled with poor lifestyles and increased stress levels, has increased the prevalence of mental health disorders over the last decade. We believe that the works undertaken as part of this project will allow patients to be more effectively managed outside the clinical environment.With this project, we are working to optimise how patient information is recorded and transmitted to the doctor. This project provides an exciting look into how machine learning can detect subtle but significant abnormalities from smartwatch data and provide that information to the doctor remotely, so they can more effectively care for their patients when they are not in the clinic/hospital---ultimately leading to improved patient safety and significant cost savings for healthcare systems.

该机器学习项目使用深度神经网络来调查包含超过100万患者的历史ECG数据库，以开发从智能手表ECG数据评估精神健康患者心脏风险的新方法。它旨在建立新的ECG特征，以提高对药物相互作用引起的心律失常的预测，并建立在该公司已建立的AI-启用心电图解释软件，已证明心脏病专家水平的心律失常检测性能。我们使用AI从智能手表ECG信号中提取细微变化，这些变化表明患者正在经历药物不良反应，他们需要立即治疗这使我们能够创建一个基于云的分析平台，在这个平台上，可以更有效地远程管理患者。我们都生活在一个繁忙的世界里，看医生和做检查都变得更加复杂。这一点，加上不良的生活方式和压力水平的增加，在过去十年中增加了心理健康疾病的患病率。我们相信，作为该项目的一部分所开展的工作将使患者在临床环境之外得到更有效的管理。通过该项目，我们正在努力优化患者信息的记录和传输方式。该项目提供了一个令人兴奋的视角，让我们了解机器学习如何从智能手表数据中检测出细微但显著的异常，并将这些信息远程提供给医生，以便他们在不在诊所/医院时更有效地照顾患者，最终提高患者安全性，并为医疗保健系统节省大量成本。