Prediction of Severe Sepsis Using SVM Model

Objectives: to evaluate the fluid responsiveness according to fluid bolus triggers and their combination in severe sepsis and septic shock. Design: observational study. Patients and Methods: patients with severe sepsis and septic shock who already received fluid after rescue phase of resuscitation. Fluid bolus (FB) was prescribed upon perceived hypovolemic manifestations: low central venous pressure (CVP), low blood pressure, tachycardia, low urine output (UOP), hyperlactatemia. FB was performed by Ringer lactate 500 ml/30 min and responsiveness was defined by increasing in stroke volume (SV) ≥15%. Results: 84 patients were enrolled, among them 30 responded to FB (35.7%). Demographic and hemodynamic profile before fluid bolus were similar between responders and non-responders, except CVP was lower in responders (7.3 ± 3.4 mmHg vs 9.2 ± 3.6 mmHg) (p 0.018). Fluid response in low CVP, low blood pressure, tachycardia, low UOP, hyperlactatemia were 48.6%, 47.4%, 38.5%, 37.0%, 36.8% making the odd ratio (OR) of these triggers were 2.81 (1.09-7.27), 1.60 (0.54-4.78), 1.89 (0.58-6.18), 1.15 (0.41-3.27) and 1.27 (0.46-3.53) respectively. Although CVP < 8 mmHg had a higher response rate, the association was not consistent at lower cut-offs. The combination of these triggers appeared to raise fluid response but did not reach statistical significance: 26.7% (1 trigger), 31.0% (2 triggers), 35.7% (3 triggers), 55.6% (4 triggers), 100% (5 triggers). Conclusions: fluid responsiveness was low in optimization phase of resuscitation. No fluid bolus trigger was superior to the others in term of providing a higher responsiveness, their combination did not improve fluid responsiveness as well.

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A case of adrenal haemorrhage in severe sepsis

Endocrine Abstracts ◽

10.1530/endoabs.38.p123 ◽

2015 ◽

Author(s):

Maria Rita Talla ◽

Alison Mackenzie

Keyword(s):

Severe Sepsis ◽

Adrenal Haemorrhage

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Sepsis, Severe Sepsis and Septic Shock in the Elderly

Korean Journal of Clinical Geriatrics ◽

10.15656/kjcg.2015.16.2.50 ◽

2015 ◽

Vol 16 (2) ◽

pp. 50-55

Author(s):

Choo-Yon Cho ◽

Sun-Hee Kim

Keyword(s):

Septic Shock ◽

Severe Sepsis ◽

The Elderly ◽

Sepsis And Septic Shock

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The Spinning Quality Control Management Based on Decision Making by Data Mining Techniques

International Journal of Emerging Research in Management and Technology ◽

10.23956/ijermt.v7i1.25 ◽

2018 ◽

Vol 7 (1) ◽

pp. 72

Author(s):

Khalid AA Abakar ◽

Chongwen Yu

Keyword(s):

Data Mining ◽

Kernel Functions ◽

Support Vector ◽

Ann Model ◽

Data Mining Techniques ◽

Yarn Quality ◽

Yarn Properties ◽

Svm Model ◽

Rbf Kernel

This work demonstrated the possibility of using the data mining techniques such as artificial neural networks (ANN) and support vector machine (SVM) based model to predict the quality of the spinning yarn parameters. Three different kernel functions were used as SVM kernel functions which are Polynomial and Radial Basis Function (RBF) and Pearson VII Function-based Universal Kernel (PUK) and ANN model were used as data mining techniques to predict yarn properties. In this paper, it was found that the SVM model based on Person VII kernel function (PUK) have the same performance in prediction of spinning yarn quality in comparison with SVM based RBF kernel. The comparison with the ANN model showed that the two SVM models give a better prediction performance than an ANN model.

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Combination of Support Vector Machine and K-Fold cross-validation for prediction of long-term degradation of the compressive strength of marine concrete

International Journal of Computational Physics Series ◽

10.29167/a1i1p120-130 ◽

2018 ◽

Vol 1 (1) ◽

pp. 120-130 ◽

Cited By ~ 1

Author(s):

Chunxiang Qian ◽

Wence Kang ◽

Hao Ling ◽

Hua Dong ◽

Chengyao Liang ◽

...

Keyword(s):

Support Vector Machine ◽

Environmental Factors ◽

Cross Validation ◽

Concrete Strength ◽

Simulation Method ◽

Support Vector ◽

Svm Model ◽

Artificial Neural Network Ann ◽

Influence Degree ◽

Fold Cross Validation

Support Vector Machine (SVM) model optimized by K-Fold cross-validation was built to predict and evaluate the degradation of concrete strength in a complicated marine environment. Meanwhile, several mathematical models, such as Artificial Neural Network (ANN) and Decision Tree (DT), were also built and compared with SVM to determine which one could make the most accurate predictions. The material factors and environmental factors that influence the results were considered. The materials factors mainly involved the original concrete strength, the amount of cement replaced by fly ash and slag. The environmental factors consisted of the concentration of Mg2+, SO42-, Cl-, temperature and exposing time. It was concluded from the prediction results that the optimized SVM model appeared to perform better than other models in predicting the concrete strength. Based on SVM model, a simulation method of variables limitation was used to determine the sensitivity of various factors and the influence degree of these factors on the degradation of concrete strength.

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