ArticleJMIR public health and surveillance2018
Real Time Influenza Monitoring Using Hospital Big Data in Combination with Machine Learning Methods: Comparison Study.
Article in JMIR public health and surveillance, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
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- A flexible framework for local-level estimation of the effective reproductive number in geographic regions with sparse data.BMC medical research methodology · 2025Article
- A Flexible Framework for Local-Level Estimation of the Effective Reproductive Number in Geographic Regions with Sparse Data.medRxiv : the preprint server for health sciences · 2025Article
- A Predictive Model of the Start of Annual Influenza Epidemics.Microorganisms · 2024Article
- Gastroenteritis Forecasting Assessing the Use of Web and Electronic Health Record Data With a Linear and a Nonlinear Approach: Comparison Study.JMIR public health and surveillance · 2023Article
- Toward the use of neural networks for influenza prediction at multiple spatial resolutions.Science advances · 2021Article
- The development and deployment of a model for hospital-level COVID-19 associated patient demand intervals from consistent estimators (DICE).Health care management science · 2021Article
- Machine Learning-Driven Models to Predict Prognostic Outcomes in Patients Hospitalized With Heart Failure Using Electronic Health Records: Retrospective Study.Journal of medical Internet research · 2021Article
- Identification of Variable Importance for Predictions of Mortality From COVID-19 Using AI Models for Ontario, Canada.Frontiers in public health · 2021Article
- Influenza forecasting for French regions combining EHR, web and climatic data sources with a machine learning ensemble approach.PloS one · 2021Article
- Multi-step ahead meningitis case forecasting based on decomposition and multi-objective optimization methods.Journal of biomedical informatics · 2020Article
- Applying Machine Learning Models with An Ensemble Approach for Accurate Real-Time Influenza Forecasting in Taiwan: Development and Validation Study.Journal of medical Internet research · 2020Article
- Infodemiology and Infoveillance: Scoping Review.Journal of medical Internet research · 2020Article
- A comparative study on predicting influenza outbreaks using different feature spaces: application of influenza-like illness data from Early Warning Alert and Response System in Syria.BMC research notes · 2020Article
- Artificial Intelligence for Surveillance in Public Health.Yearbook of medical informatics · 2019Article
- Google Trends in Infodemiology and Infoveillance: Methodology Framework.JMIR public health and surveillance · 2019Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTraditional surveillance systems produce estimates of influenza-like illness (ILI) incidence rates, but with 1- to 3-week delay. Accurate real-time monitoring systems for influenza outbreaks could be useful for making public health decisions. Several studies have investigated the possibility of using internet users' activity data and different statistical models to predict influenza epidemics in near real time. However, very few studies have investigated hospital big data.
objectiveHere, 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.
methodsWe 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).
resultsFor 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.
conclusionsWe 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.
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