Prediction of severe sepsis using SVM model.
Sepsis is an infectious condition that results in damage to organs. This paper proposes a severe sepsis model based on Support Vector Machine (SVM) for predicting whether a septic patient will become severe sepsis. We chose several clinical physiology of sepsis for identifying the features used for SVM. Based on the model, a medical decision support system is proposed for clinical diagnosis. The results show that the prognosis of a septic patient can be more precisely predicted than ever. We conduct several experiments, whose results demonstrate that the proposed model provides high accuracy and high sensitivity and can be used as a reminding system to provide in-time treatment in ICU.
Wang
Shu-Li
SL
National Chung-Cheng University, Chia-Yi, Taiwan.
Wu
Fan
F
Wang
Bo-Hang
BH
D018806:APACHE; D000465:Algorithms; D001185:Artificial Intelligence; D019295:Computational Biology; D003661:Decision Support Techniques; D003936:Diagnosis, Computer-Assisted; D006801:Humans; D007362:Intensive Care Units; D011379:Prognosis; D018805:Sepsis / Q000175:diagnosis*