Wearable Devices, Smartphones, and Interpretable Artificial Intelligence in Combating COVID-19.
Wearable Devices, Smartphones, and Interpretable Artificial Intelligence in Combating COVID-19.
复制标题
DOI：
10.3390/s21248424
复制
发表时间：
2021-12-17
期刊：
影响因子：
--
通讯作者：
Nasir Q
中科院分区：
文献类型：
--
作者：
Hijazi H;Abu Talib M;Hasasneh A;Bou Nassif A;Ahmed N;Nasir Q
心率变异性 (HRV) 和每分钟心跳次数 (BPM) 等生理指标可以作为呼吸道感染的有力健康指标。 HRV 和 BPM 可以通过广泛使用的腕戴式生物识别可穿戴设备和智能手机来获取。这些指标的连续异常变化可能是呼吸道感染（例如 COVID-19）的早期征兆。因此，可穿戴设备和智能手机应该通过其他上下文数据和人工智能 (AI) 技术支持的早期检测，在抗击 COVID-19 方面发挥重要作用。在本文中，我们研究了从可穿戴设备和智能手机收集的心脏测量值（即 HRV 和 BPM）在展示对 COVID-19 的炎症反应早期发作中的作用。 AI 框架由两个模块组成：一个可解释的预测模型，用于对 HRV 测量状态（正常或受炎症影响）进行分类；以及一个循环神经网络 (RNN)，用于分析用户的日常状态（即移动应用程序中的文本日志）。综合两个分类决策，生成“可能感染了 COVID-19”或“没有明显感染迹象”的最终决策。我们使用了一个公开的数据集，其中包含 186 名患者，拥有超过 3200 个 HRV 读数和大量用户文本日志。该方法的首次评估显示，在症状出现前两天预测感染的准确度为 83.34 ± 1.68%，精确度、召回率和 F1 分数分别为 0.91、0.88、0.89，并由使用局部可解释模型不可知论解释 (LIME) 的模型解释支持。
Physiological measures, such as heart rate variability (HRV) and beats per minute (BPM), can be powerful health indicators of respiratory infections. HRV and BPM can be acquired through widely available wrist-worn biometric wearables and smartphones. Successive abnormal changes in these indicators could potentially be an early sign of respiratory infections such as COVID-19. Thus, wearables and smartphones should play a significant role in combating COVID-19 through the early detection supported by other contextual data and artificial intelligence (AI) techniques. In this paper, we investigate the role of the heart measurements (i.e., HRV and BPM) collected from wearables and smartphones in demonstrating early onsets of the inflammatory response to the COVID-19. The AI framework consists of two blocks: an interpretable prediction model to classify the HRV measurements status (as normal or affected by inflammation) and a recurrent neural network (RNN) to analyze users’ daily status (i.e., textual logs in a mobile application). Both classification decisions are integrated to generate the final decision as either “potentially COVID-19 infected” or “no evident signs of infection”. We used a publicly available dataset, which comprises 186 patients with more than 3200 HRV readings and numerous user textual logs. The first evaluation of the approach showed an accuracy of 83.34 ± 1.68% with 0.91, 0.88, 0.89 precision, recall, and F1-Score, respectively, in predicting the infection two days before the onset of the symptoms supported by a model interpretation using the local interpretable model-agnostic explanations (LIME).