Real-Time Predictive Analytics for Sepsis Level and Therapeutic Plans in Intensive Care Medicine

@article{Gonalves2014RealTimePA,
  title={Real-Time Predictive Analytics for Sepsis Level and Therapeutic Plans in Intensive Care Medicine},
  author={Jo{\~a}o M. C. Gonçalves and Filipe Portela and Manuel Filipe Santos and {\'A}lvaro M. Silva and J. Machado and Ant{\'o}nio Abelha and Fernando Rua},
  journal={Int. J. Heal. Inf. Syst. Informatics},
  year={2014},
  volume={9},
  pages={36-54}
}
Optimal treatments for patients with microbiological problems depend significantly on the ability of the attending physicians to predict sepsis level. A set of Data Mining (DM) models has been developed using forecasting techniques and classification models to aid decision making by physicians about the appropriate, and most effective, therapeutic plan to adopt in specific situations. A combination of Decision Trees, Support Vector Machines and Naive Bayes classifier were being used to generate… 
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