@文章{信息:doi/10.2196/11361，作者="Poirier, Canelle和Lavenu, Audrey和Bertaud, Val{\'e}rie和Campillo-Gimenez, Boris和Chazard, Emmanuel和Cuggia, Marc和Bouzill{\'e}， Guillaume"，标题="利用医院大数据结合机器学习方法进行实时流感监测:比较研究"，期刊="JMIR公共卫生监测"，年="2018"，月=" 12月"，日="21"，卷="4"，数="4"，页="e11361"，关键词="电子健康记录;大数据;infodemiology;infoveillance;流感;机器学习;背景:传统的监测系统产生流感样疾病(ILI)发病率的估计，但有1- 3周的延迟。准确的流感疫情实时监测系统可能有助于制定公共卫生决策。有几项研究调查了利用互联网用户的活动数据和不同的统计模型来预测流感流行的可能性。然而，对医院大数据的研究很少。 Objective: Here, we compared internet and electronic health records (EHRs) data and different statistical models to identify the best approach (data type and statistical model) for ILI estimates in real time. Methods: We used Google data for internet data and the clinical data warehouse eHOP, which included all EHRs from Rennes University Hospital (France), for hospital data. We compared 3 statistical models---random forest, elastic net, and support vector machine (SVM). Results: For national ILI incidence rate, the best correlation was 0.98 and the mean squared error (MSE) was 866 obtained with hospital data and the SVM model. For the Brittany region, the best correlation was 0.923 and MSE was 2364 obtained with hospital data and the SVM model. Conclusions: We found that EHR data together with historical epidemiological information (French Sentinelles network) allowed for accurately predicting ILI incidence rates for the entire France as well as for the Brittany region and outperformed the internet data whatever was the statistical model used. Moreover, the performance of the two statistical models, elastic net and SVM, was comparable. ", issn="2369-2960", doi="10.2196/11361", url="http://publichealth.www.mybigtv.com/2018/4/e11361/", url="https://doi.org/10.2196/11361", url="http://www.ncbi.nlm.nih.gov/pubmed/30578212" }