TY -的盟Choo Hyunwoo AU -金,Myeongchan盟——崔Jiyun AU - Shin Jaewon AU - Shin Soo-Yong PY - 2020 DA - 2020/10/29 TI -流感筛选通过深度学习使用流行病学和我们健康数据的结合:开发和验证研究乔- J地中海互联网Res SP - e21369六世- 22 - 10 KW -流感KW -筛查工具KW -我们相信健康数据千瓦移动健康KW - mHealth KW -深度学习AB -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，初级保健中的流感筛查具有挑战性。目的:本研究的目的是开发一种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。每一集都包含用户的年龄、性别、体重和至少一项体温记录。总发病数为6657。在这些病例中，有3326例被诊断为流感。我们将这些事件分为80%的训练集(2664/3330)和20%的测试集(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. SN - 1438-8871 UR - //www.mybigtv.com/2020/10/e21369/ UR - https://doi.org/10.2196/21369 UR - http://www.ncbi.nlm.nih.gov/pubmed/33118941 DO - 10.2196/21369 ID - info:doi/10.2196/21369 ER -