A comprehensive health classification model based on support vector machine for proseal laryngeal mask and tracheal catheter assessment in herniorrhaphy.

Purpose: In order to classify different types of health data collected in clinical practice of hernia surgery more effectively and improve the classification performance of support vector machine (SVM). Methods: A prospective randomized study was conducted. Sixty patients undergoing hernia repair under general anesthesia were randomly divided into two groups, PLMA group (n = 30) and ETT group (n = 30), for airway management. Heart rate, systolic blood pressure, diastolic blood pressure, mean arterial pressure, respiratory parameters and the incidence of complications related to ProSeal laryngeal mask airway (PLMA) and endotracheal tube (ETT) were collected in clinical experiments in order to evaluate the operation condition. On the basis of this experiment, at first, expert credibility is introduced to process the index value; secondly, the classification weight of the index is objectively determined by the information entropy output of the index itself; finally, a comprehensive classification model of support vector machine based on key sample set is proposed and its advantages are evaluated. Result: After classifying the experimental data, we found that SVM can accurately judge the effect of surgery by data. In this experiment, PLMA method is better than ETT method in xenon repair operation. Discussion: SVM has great accuracy and practicability in judging the outcome of xenon repair operation. Conclusion: The proposed index classification weight model can deal with the uncertainties caused by uncertain information and give the confidence of the uncertain information. Compared with the traditional SVM method, the proposed method based on SVM and key sample set greatly reduces the number of samples that misjudge the effect of samples, and improves the practicability of SVM method. It is concluded that PLMA is superior to the ETT technique to hernia surgical. The idea of constructing classification model based on key sample set proposed in this paper can also be used for reference in other data mining methods.

Comparison of stroke volume variation with pulse pressure variation as a diagnostic indicator of fluid responsiveness in mechanically ventilated critically ill patients.

OBJECTIVE: To compare the diagnostic accuracy of stroke volume variation (SVV) and pulse pressure variation (PPV) in studies that examined both parameters in the same patient population. METHODS: Literature search was conducted in PubMed, EMBASE, CINAHL, and Google Scholar. Receiver operator characteristic (ROC) curves were examined, and summary ROC curves were plotted. RESULTS: The study was conducted from January to July 2013 in The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, China. The meta-analysis of 19 studies published during the years 2005 and 2013 revealed a high degree of diagnostic accuracy of both SVV and PPV in predicting fluid responsiveness. The sensitivity and specificity of both the parameters were observed above 80% in a heterogeneous group of over 850 patients of which 55% responded to fluid challenge. The following values along with 95% confidence interval were noticed: SVV - sensitivity 82 (59-93%) and specificity 84 (62-95%), PPV - sensitivity 84 (62-95%) and specificity 83 (58-94%). Area under the curve values obtained in the pooled analysis were 0.84 (0.79-0.89) for SVV, and 0.88 (0.84-0.92) for PPV. CONCLUSION: Both SVV and PPV exhibit a high degree of diagnostic accuracy in predicting the success or failure of a fluid challenge in hemodynamically unstable critically ill patients under controlled mechanical ventilation.