@文章{信息:doi/10.2196/21369，作者=“Choo, Hyunwoo and Kim, Myeongchan and Choi, Jiyun and Shin, Jaewon and Shin, Soo-Yong”，标题=“利用流行病学和患者生成的健康数据结合的深度学习进行流感筛查:开发和验证研究”，期刊=“J Med Internet Res”，年=“2020”，月=“10”，日=“29”，卷=“22”，数=“10”，页=“e21369”，关键词=“流感;检测工具;患者生成的健康数据;移动健康;移动健康;背景:由于快速抗原检测的低敏感性和缺乏适当的筛查试验，在初级保健中筛查流感具有挑战性。目的:本研究的目的是开发一个基于机器学习的筛查工具，使用从移动健康(mHealth)应用程序获得的患者生成的健康数据(PGHD)。方法:我们训练了一个基于门控复发单元的深度学习模型，使用PGHD筛查流感，包括每个患者的发烧模式和给药记录。我们使用气象数据和基于应用程序的每周流感患者数量监测。我们将单次发作定义为连续天数的集合，包括用户被诊断患有流感或其他疾病的那一天。用户在上一个记录24小时后输入的任何记录都被认为是新一集的开始。 Each episode contained data on the user's age, gender, weight, and at least one body temperature record. The total number of episodes was 6657. Of these, there were 3326 episodes within which influenza was diagnosed. We divided these episodes into 80{\%} training sets (2664/3330) and 20{\%} test sets (666/3330). A 5-fold cross-validation was used on the training set. Results: We achieved reliable performance with an accuracy of 82{\%}, a sensitivity of 84{\%}, and a specificity of 80{\%} in the test set. After the effect of each input variable was evaluated, app-based surveillance was observed to be the most influential variable. The correlation between the duration of input data and performance was not statistically significant (P=.09). Conclusions: These findings suggest that PGHD from an mHealth app could be a complementary tool for influenza screening. In addition, PGHD, along with traditional clinical data, could be used to improve health conditions. ", issn="1438-8871", doi="10.2196/21369", url="//www.mybigtv.com/2020/10/e21369/", url="https://doi.org/10.2196/21369", url="http://www.ncbi.nlm.nih.gov/pubmed/33118941" }