tetano
Editor, Senior Moderator
PLoS One. 2019 Apr 23;14(4):e0215600. doi: 10.1371/journal.pone.0215600. eCollection 2019.
[h=1]Regional level influenza study based on Twitter and machine learning method.[/h] Xue H[SUP]1,[/SUP][SUP]2[/SUP], Bai Y[SUP]2[/SUP], Hu H[SUP]2[/SUP], Liang H[SUP]3[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] The significance of flu prediction is that the appropriate preventive and control measures can be taken by relevant departments after assessing predicted data; thus, morbidity and mortality can be reduced. In this paper, three flu prediction models, based on twitter and US Centers for Disease Control's (CDC's) Influenza-Like Illness (ILI) data, are proposed (models 1-3) to verify the factors that affect the spread of the flu. In this work, an Improved Particle Swarm Optimization algorithm to optimize the parameters of Support Vector Regression (IPSO-SVR) was proposed. The IPSO-SVR was trained by the independent and dependent variables of the three models (models 1-3) as input and output. The trained IPSO-SVR method was used to predict the regional unweighted percentage ILI (%ILI) events in the US. The prediction results of each model are analyzed and compared. The results show that the IPSO-SVR method (model 3) demonstrates excellent performance in real-time prediction of ILIs, and further highlights the benefits of using real-time twitter data, thus providing an effective means for the prevention and control of flu.
PMID: 31013324 DOI: 10.1371/journal.pone.0215600
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Text-to-speech function is limited to 200 characters
[h=1]Regional level influenza study based on Twitter and machine learning method.[/h] Xue H[SUP]1,[/SUP][SUP]2[/SUP], Bai Y[SUP]2[/SUP], Hu H[SUP]2[/SUP], Liang H[SUP]3[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] The significance of flu prediction is that the appropriate preventive and control measures can be taken by relevant departments after assessing predicted data; thus, morbidity and mortality can be reduced. In this paper, three flu prediction models, based on twitter and US Centers for Disease Control's (CDC's) Influenza-Like Illness (ILI) data, are proposed (models 1-3) to verify the factors that affect the spread of the flu. In this work, an Improved Particle Swarm Optimization algorithm to optimize the parameters of Support Vector Regression (IPSO-SVR) was proposed. The IPSO-SVR was trained by the independent and dependent variables of the three models (models 1-3) as input and output. The trained IPSO-SVR method was used to predict the regional unweighted percentage ILI (%ILI) events in the US. The prediction results of each model are analyzed and compared. The results show that the IPSO-SVR method (model 3) demonstrates excellent performance in real-time prediction of ILIs, and further highlights the benefits of using real-time twitter data, thus providing an effective means for the prevention and control of flu.
PMID: 31013324 DOI: 10.1371/journal.pone.0215600
Free full text
[TABLE="cellspacing: 1"]
[TR]
[TD="class: SL, width: 10%, align: right"][/TD]
[TD="class: SL, width: 20%, align: left"]Detect languageundefined[/TD]
[TD="class: SL, width: 3, align: center"]
[/TD]
[TD="class: SL, width: 20%, align: left"]undefined[/TD]
[TD="class: SL, width: 8%, align: center"]
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[TD="class: SL, width: 8%, align: center"]
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[/TABLE]
Text-to-speech function is limited to 200 characters