Real Time Influenza Monitoring Using Hospital Big Data in Combination with Machine Learning Methods: Comparison Study. Poirier, C., Lavenu, A., Bertaud, V., Campillo-Gimenez, B., Chazard, E., Cuggia, M., & Bouzillé, G. JMIR public health and surveillance, 4(4):e11361, December, 2018. doi abstract bibtex BACKGROUND: Traditional 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. 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.
@article{poirier_real_2018,
title = {Real {Time} {Influenza} {Monitoring} {Using} {Hospital} {Big} {Data} in {Combination} with {Machine} {Learning} {Methods}: {Comparison} {Study}},
volume = {4},
issn = {2369-2960},
shorttitle = {Real {Time} {Influenza} {Monitoring} {Using} {Hospital} {Big} {Data} in {Combination} with {Machine} {Learning} {Methods}},
doi = {10.2196/11361},
abstract = {BACKGROUND: Traditional 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.
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.},
language = {eng},
number = {4},
journal = {JMIR public health and surveillance},
author = {Poirier, Canelle and Lavenu, Audrey and Bertaud, Valérie and Campillo-Gimenez, Boris and Chazard, Emmanuel and Cuggia, Marc and Bouzillé, Guillaume},
month = dec,
year = {2018},
pmid = {30578212},
keywords = {Sentinelles network, big data, electronic health records, influenza, infodemiology, infoveillance, machine learning},
pages = {e11361},
}
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{"_id":"dSaFhRJK2Lr9rQ2nv","bibbaseid":"poirier-lavenu-bertaud-campillogimenez-chazard-cuggia-bouzill-realtimeinfluenzamonitoringusinghospitalbigdataincombinationwithmachinelearningmethodscomparisonstudy-2018","author_short":["Poirier, C.","Lavenu, A.","Bertaud, V.","Campillo-Gimenez, B.","Chazard, E.","Cuggia, M.","Bouzillé, G."],"bibdata":{"bibtype":"article","type":"article","title":"Real Time Influenza Monitoring Using Hospital Big Data in Combination with Machine Learning Methods: Comparison Study","volume":"4","issn":"2369-2960","shorttitle":"Real Time Influenza Monitoring Using Hospital Big Data in Combination with Machine Learning Methods","doi":"10.2196/11361","abstract":"BACKGROUND: Traditional 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. 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.","language":"eng","number":"4","journal":"JMIR public health and surveillance","author":[{"propositions":[],"lastnames":["Poirier"],"firstnames":["Canelle"],"suffixes":[]},{"propositions":[],"lastnames":["Lavenu"],"firstnames":["Audrey"],"suffixes":[]},{"propositions":[],"lastnames":["Bertaud"],"firstnames":["Valérie"],"suffixes":[]},{"propositions":[],"lastnames":["Campillo-Gimenez"],"firstnames":["Boris"],"suffixes":[]},{"propositions":[],"lastnames":["Chazard"],"firstnames":["Emmanuel"],"suffixes":[]},{"propositions":[],"lastnames":["Cuggia"],"firstnames":["Marc"],"suffixes":[]},{"propositions":[],"lastnames":["Bouzillé"],"firstnames":["Guillaume"],"suffixes":[]}],"month":"December","year":"2018","pmid":"30578212","keywords":"Sentinelles network, big data, electronic health records, influenza, infodemiology, infoveillance, machine learning","pages":"e11361","bibtex":"@article{poirier_real_2018,\n\ttitle = {Real {Time} {Influenza} {Monitoring} {Using} {Hospital} {Big} {Data} in {Combination} with {Machine} {Learning} {Methods}: {Comparison} {Study}},\n\tvolume = {4},\n\tissn = {2369-2960},\n\tshorttitle = {Real {Time} {Influenza} {Monitoring} {Using} {Hospital} {Big} {Data} in {Combination} with {Machine} {Learning} {Methods}},\n\tdoi = {10.2196/11361},\n\tabstract = {BACKGROUND: Traditional 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.\nOBJECTIVE: 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.\nMETHODS: 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).\nRESULTS: 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.\nCONCLUSIONS: 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.},\n\tlanguage = {eng},\n\tnumber = {4},\n\tjournal = {JMIR public health and surveillance},\n\tauthor = {Poirier, Canelle and Lavenu, Audrey and Bertaud, Valérie and Campillo-Gimenez, Boris and Chazard, Emmanuel and Cuggia, Marc and Bouzillé, Guillaume},\n\tmonth = dec,\n\tyear = {2018},\n\tpmid = {30578212},\n\tkeywords = {Sentinelles network, big data, electronic health records, influenza, infodemiology, infoveillance, machine learning},\n\tpages = {e11361},\n}\n\n","author_short":["Poirier, C.","Lavenu, A.","Bertaud, V.","Campillo-Gimenez, B.","Chazard, E.","Cuggia, M.","Bouzillé, G."],"key":"poirier_real_2018","id":"poirier_real_2018","bibbaseid":"poirier-lavenu-bertaud-campillogimenez-chazard-cuggia-bouzill-realtimeinfluenzamonitoringusinghospitalbigdataincombinationwithmachinelearningmethodscomparisonstudy-2018","role":"author","urls":{},"keyword":["Sentinelles network","big data","electronic health records","influenza","infodemiology","infoveillance","machine learning"],"metadata":{"authorlinks":{}}},"bibtype":"article","biburl":"https://api.zotero.org/users/1597782/collections/MSB7W4UM/items?key=gxIPM4PJtMVcB8OpssCWodtP&format=bibtex&limit=100&start=100&sort=date","dataSources":["Ad3P6FkzWSCKrZQXc"],"keywords":["sentinelles network","big data","electronic health records","influenza","infodemiology","infoveillance","machine learning"],"search_terms":["real","time","influenza","monitoring","using","hospital","big","data","combination","machine","learning","methods","comparison","study","poirier","lavenu","bertaud","campillo-gimenez","chazard","cuggia","bouzillé"],"title":"Real Time Influenza Monitoring Using Hospital Big Data in Combination with Machine Learning Methods: Comparison Study","year":2018}