基于序列深度学习的纵向咳嗽、呼吸和语音数据在COVID-19进展预测中卡塔尔世界杯8强波胆分析的应用模型开发与验证%A Dang,Ting %A Han,Jing %A Xia,Tong %A Spathis,Dimitris %A Bondareva,Erika %A Siegele-Brown,Chloë %A Chauhan,Jagmohan %A Grammenos,Andreas %A Hasthanasombat,Apinan %A Floto,R Andres %A Cicuta,Pietro %A Mascolo,Cecilia %+剑桥大学计算机科学与技术系，英国剑桥大学，cb30fd, 44 7895587796，td464@cam.ac.uk %K COVID-19 %K音频%K COVID-19进展%K深度学习%K移动健康%K纵向研究%D 2022 %7 21.6.2022 %9背景:最近的工作显示了使用音频数据(如咳嗽、呼吸和声音)筛查COVID-19的潜力。然而，鉴于目前的音频样本，这些方法仅侧重于一次性检测和检测感染，而不监测COVID-19的疾病进展。通过纵向音频数据持续监测COVID-19的进展，特别是恢复情况，提出了有限的探索。跟踪疾病进展特征和恢复模式可以带来见解，并导致更及时的治疗或治疗调整，以及更好的卫生保健系统资源管理。目的:本研究的主要目的是探索纵向音频样本随时间变化对COVID-19进展预测的潜力，特别是使用顺序深度学习技术进行恢复趋势预测。方法:分析了212人在5-385天内的呼吸音频数据，包括呼吸、咳嗽和语音样本，以及他们自我报告的COVID-19检测结果。我们开发并验证了一种使用门控循环单元(gru)的深度学习跟踪工具，通过探索个体历史音频生物标志物的音频动态来检测COVID-19的进展。调查包括两个部分: (1) COVID-19 detection in terms of positive and negative (healthy) tests using sequential audio signals, which was primarily assessed in terms of the area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity, with 95% CIs, and (2) longitudinal disease progression prediction over time in terms of probability of positive tests, which was evaluated using the correlation between the predicted probability trajectory and self-reported labels. Results: We first explored the benefits of capturing longitudinal dynamics of audio biomarkers for COVID-19 detection. The strong performance, yielding an AUROC of 0.79, a sensitivity of 0.75, and a specificity of 0.71 supported the effectiveness of the approach compared to methods that do not leverage longitudinal dynamics. We further examined the predicted disease progression trajectory, which displayed high consistency with longitudinal test results with a correlation of 0.75 in the test cohort and 0.86 in a subset of the test cohort with 12 (57.1%) of 21 COVID-19–positive participants who reported disease recovery. Our findings suggest that monitoring COVID-19 evolution via longitudinal audio data has potential in the tracking of individuals’ disease progression and recovery. Conclusions: An audio-based COVID-19 progression monitoring system was developed using deep learning techniques, with strong performance showing high consistency between the predicted trajectory and the test results over time, especially for recovery trend predictions. This has good potential in the postpeak and postpandemic era that can help guide medical treatment and optimize hospital resource allocations. The changes in longitudinal audio samples, referred to as audio dynamics, are associated with COVID-19 progression; thus, modeling the audio dynamics can potentially capture the underlying disease progression process and further aid COVID-19 progression prediction. This framework provides a flexible, affordable, and timely tool for COVID-19 tracking, and more importantly, it also provides a proof of concept of how telemonitoring could be applicable to respiratory diseases monitoring, in general. %M 35653606 %R 10.2196/37004 %U //www.mybigtv.com/2022/6/e37004 %U https://doi.org/10.2196/37004 %U http://www.ncbi.nlm.nih.gov/pubmed/35653606