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.
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Wang, SL., Wu, F., Wang, BH. (2010). Prediction of Severe Sepsis Using SVM Model. In: Arabnia, H. (eds) Advances in Computational Biology. Advances in Experimental Medicine and Biology, vol 680. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-5913-3_9
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DOI: https://doi.org/10.1007/978-1-4419-5913-3_9
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