tetano
Editor, Senior Moderator
Sci Rep. 2018 Mar 20;8(1):4895. doi: 10.1038/s41598-018-23075-1.
[h=1]Prediction of influenza-like illness based on the improved artificial tree algorithm and artificial neural network.[/h] Hu H[SUP]1[/SUP], Wang H[SUP]2[/SUP], Wang F[SUP]2[/SUP], Langley D[SUP]2[/SUP], Avram A[SUP]2[/SUP], Liu M[SUP]3[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] Because influenza is a contagious respiratory illness that seriously threatens public health, accurate real-time prediction of influenza outbreaks may help save lives. In this paper, we use the Twitter data set and the United States Centers for Disease Control's influenza-like illness (ILI) data set to predict a nearly real-time regional unweighted percentage ILI in the United States by use of an artificial neural network optimized by the improved artificial tree algorithm. The results show that the proposed method is an efficient approach to real-time prediction.
PMID: 29559649 DOI: 10.1038/s41598-018-23075-1
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[h=1]Prediction of influenza-like illness based on the improved artificial tree algorithm and artificial neural network.[/h] Hu H[SUP]1[/SUP], Wang H[SUP]2[/SUP], Wang F[SUP]2[/SUP], Langley D[SUP]2[/SUP], Avram A[SUP]2[/SUP], Liu M[SUP]3[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] Because influenza is a contagious respiratory illness that seriously threatens public health, accurate real-time prediction of influenza outbreaks may help save lives. In this paper, we use the Twitter data set and the United States Centers for Disease Control's influenza-like illness (ILI) data set to predict a nearly real-time regional unweighted percentage ILI in the United States by use of an artificial neural network optimized by the improved artificial tree algorithm. The results show that the proposed method is an efficient approach to real-time prediction.
PMID: 29559649 DOI: 10.1038/s41598-018-23075-1
Free full text