title: Prediction of severe sepsis using SVM model.
abstract: 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.
authors.affiliation: National Chung-Cheng University, Chia-Yi, Taiwan.
authors.forename: Shu-Li
authors.initials: SL
authors.lastname: Wang
authors.forename: Fan
authors.initials: F
authors.lastname: Wu
authors.forename: Bo-Hang
authors.initials: BH
authors.lastname: Wang
mesh_terms: 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*