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

这个机器学习项目使用深度神经网络来研究包含100多万患者的历史心电数据库，以开发从智能手表心电数据评估精神健康患者心脏风险的新方法。它旨在建立新的心电特征，改善由于药物相互作用而导致的心律失常的预测，并建立在该公司建立的支持人工智能的心电解释软件的基础上，该软件已经展示了心脏病医生级别的心律失常检测性能。我们使用人工智能从智能手表的心电信号中提取细微变化，这些信号表明患者对药物的不良反应，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，在其中可以更有效地远程管理患者。我们都生活在一个忙碌的世界里，看病和检查更加复杂。这一点，再加上糟糕的生活方式和不断增加的压力水平，在过去十年里增加了精神健康障碍的患病率。我们相信，作为这个项目的一部分进行的工作将使患者在临床环境之外得到更有效的管理。通过这个项目，我们正在努力优化患者信息的记录和传输给医生的方式。该项目提供了一个令人兴奋的视角，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，以便他们可以在患者不在诊所/医院时更有效地护理他们的患者-最终提高患者的安全性并显著节省医疗保健系统的成本。

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.