Evaluation of the POSSUM, p-POSSUM, o-POSSUM, and APACHE II scoring systems in predicting postoperative mortality and morbidity in gastric cancer patients

SummaryBackground/ObjectiveGastric cancer is the fourth most prevalent cancer worldwide. The ability to accurately predict surgery-related morbidity and mortality is critical in deciding both the timing of surgery and choice of surgical procedure. The aim of this study is to compare the POSSUM, p-POSSUM, o-POSSUM, and APACHE II scoring systems for predicting surgical morbidity and mortality in Chinese gastric cancer patients, as well as to create new scoring systems to achieve better prediction.MethodsData from 612 gastric cancer patients undergoing gastrectomy between January 2007 and December 2011 were included in this study. The predictive abilities of the four scoring systems were compared by examining observed-to-expected (O/E) ratios, the receiver operating characteristic curve, Student t test, and χ2 test results.ResultsThe observed complication rate of 34% (n = 208) did not differ significantly from the rate of 36.6% (n = 208) predicted by the POSSUM scoring system (O/E ratio = 0.93). The observed mortality rate was 2.9% (n = 18). For predicting mortality, POSSUM had an O/E ratio of 0.34 as compared with p-POSSUM (O/E ratio = 0.91), o-POSSUM (O/E ratio = 1.26), and APACHE II (O/E ratio = 0.28).ConclusionThe POSSUM scoring system performed well with respect to predicting morbidity risk following gastric cancer resection. For predicting postoperative mortality, p-POSSUM and o-POSSUM exhibited superior performance relative to POSSUM and APACHE II

Bearing degradation assessment based on entropy with time parameter and fuzzy c-means clustering

Bearings are one of the most crucial elements in rotating machine. The condition of bearings decides the operation of machine. Consequently, it is necessary to study the assessment of bearing degradation in order to develop condition-based maintenance. This paper improves an indicator based on entropy which is calculated by wavelet packet decomposition and auto-regressive model. By introducing time parameter, the indicator solves the problem of instability in the initial stage of operation and it is less influenced by the operational conditions. Then, fuzzy c-means clustering can evaluate the process of degradation. Moreover, it can provide the threshold adaptively and help to repair by unit replacement. To ensure the applicability, the data of this paper comes from two laboratories, FEMTO-ST Institute and Intelligent Maintenance System Center. The result indicates that the method is effective to assess bearing degradation process

A novel faults detection method for rolling bearing based on RCMDE and ISVM

The rolling bearing is an essential element widely used in the rotating machinery. Bearing failures are among the main reasons for breakdown of rotating machinery. Therefore, fault detection of bearing is necessary to reduce the probability of breakdown and safety accidents. A novel fault diagnosis method for rolling bearing based on Refined Composite Multiscale Dispersion Entropy (RCMDE) and Improved Support Vector Machine (ISVM) is presented in this paper. The RCMDE is a new irregular index in biomedical signal analysis, which has lower computational cost and more stable results. Therefore, the RCMDE is introduced as fault feature to represent the bearing fault characteristics. After feature extraction, an improved support vector machine based on whale optimization algorithm (WOA) and support vector machine (SVM) is proposed as a fault classifier, which has the advantages of less training samples and good classification effect. The effectiveness of the proposed method in bearing fault diagnosis is verified by using bearing fault experimental data

A new fault diagnosis method using deep belief network and compressive sensing

Compressive sensing provides a new idea for machinery monitoring, which greatly reduces the burden on data transmission. After that, the compressed signal will be used for fault diagnosis by feature extraction and fault classification. However, traditional fault diagnosis heavily depends on the prior knowledge and requires a signal reconstruction which will cost great time consumption. For this problem, a deep belief network (DBN) is used here for fault detection directly on compressed signal. This is the first time DBN is combined with the compressive sensing. The PCA analysis shows that DBN has successfully separated different features. The DBN method which is tested on compressed gearbox signal, achieves 92.5 % accuracy for 25 % compressed signal. We compare the DBN on both compressed and reconstructed signal, and find that the DBN using compressed signal not only achieves better accuracies, but also costs less time when compression ratio is less than 0.35. Moreover, the results have been compared with other classification methods

A new fault diagnosis method using deep belief network and compressive sensing

Compressive sensing provides a new idea for machinery monitoring, which greatly reduces the burden on data transmission. After that, the compressed signal will be used for fault diagnosis by feature extraction and fault classification. However, traditional fault diagnosis heavily depends on the prior knowledge and requires a signal reconstruction which will cost great time consumption. For this problem, a deep belief network (DBN) is used here for fault detection directly on compressed signal. This is the first time DBN is combined with the compressive sensing. The PCA analysis shows that DBN has successfully separated different features. The DBN method which is tested on compressed gearbox signal, achieves 92.5 % accuracy for 25 % compressed signal. We compare the DBN on both compressed and reconstructed signal, and find that the DBN using compressed signal not only achieves better accuracies, but also costs less time when compression ratio is less than 0.35. Moreover, the results have been compared with other classification methods

Evaluation of the POSSUM, p-POSSUM, o-POSSUM, and APACHE II scoring systems in predicting postoperative mortality and morbidity in gastric cancer patients

Gastric cancer is the fourth most prevalent cancer worldwide. The ability to accurately predict surgery-related morbidity and mortality is critical in deciding both the timing of surgery and choice of surgical procedure. The aim of this study is to compare the POSSUM, p-POSSUM, o-POSSUM, and APACHE II scoring systems for predicting surgical morbidity and mortality in Chinese gastric cancer patients, as well as to create new scoring systems to achieve better prediction. Data from 612 gastric cancer patients undergoing gastrectomy between January 2007 and December 2011 were included in this study. The predictive abilities of the four scoring systems were compared by examining observed-to-expected (O/E) ratios, the receiver operating characteristic curve, Student t test, and χ2 test results. The observed complication rate of 34% (n = 208) did not differ significantly from the rate of 36.6% (n = 208) predicted by the POSSUM scoring system (O/E ratio = 0.93). The observed mortality rate was 2.9% (n = 18). For predicting mortality, POSSUM had an O/E ratio of 0.34 as compared with p-POSSUM (O/E ratio = 0.91), o-POSSUM (O/E ratio = 1.26), and APACHE II (O/E ratio = 0.28). The POSSUM scoring system performed well with respect to predicting morbidity risk following gastric cancer resection. For predicting postoperative mortality, p-POSSUM and o-POSSUM exhibited superior performance relative to POSSUM and APACHE II

Finite-Time Distributed Control of Non-Triangular Stochastic Nonlinear Multi-Agent Systems with Input Constraints

This paper investigates the problem of finite-time distributed consensus control for non-triangular stochastic nonlinear multi-agent systems (SNMASs) with input constraints. Fuzzy logical systems are used to identify the unknown nonlinear dynamics of non-triangular SNMASs. A finite-time command filter is utilized to eliminate the issue of “explosion of complexity” in the conventional backstepping-based distributed control algorithm, and a fractional power error compensation mechanism is constructed to improve the distributed control performance of SNMASs. It is proved that the proposed distributed controller enables all of the closed-loop system’s signals to be semi-globally finite-time bounded in probability, and the consensus tracking errors will converge to a sufficiently small neighborhood of the origin in a finite time. Finally, the effectiveness of the presented finite-time distributed control scheme is illustrated with a simulated example

Evaluation of the POSSUM, p-POSSUM, o-POSSUM, and APACHE II scoring systems in predicting postoperative mortality and morbidity in gastric cancer patients

Background/Objective: Gastric cancer is the fourth most prevalent cancer worldwide. The ability to accurately predict surgery-related morbidity and mortality is critical in deciding both the timing of surgery and choice of surgical procedure. The aim of this study is to compare the POSSUM, p-POSSUM, o-POSSUM, and APACHE II scoring systems for predicting surgical morbidity and mortality in Chinese gastric cancer patients, as well as to create new scoring systems to achieve better prediction. Methods: Data from 612 gastric cancer patients undergoing gastrectomy between January 2007 and December 2011 were included in this study. The predictive abilities of the four scoring systems were compared by examining observed-to-expected (O/E) ratios, the receiver operating characteristic curve, Student t test, and χ2 test results. Results: The observed complication rate of 34% (n = 208) did not differ significantly from the rate of 36.6% (n = 208) predicted by the POSSUM scoring system (O/E ratio = 0.93). The observed mortality rate was 2.9% (n = 18). For predicting mortality, POSSUM had an O/E ratio of 0.34 as compared with p-POSSUM (O/E ratio = 0.91), o-POSSUM (O/E ratio = 1.26), and APACHE II (O/E ratio = 0.28). Conclusion: The POSSUM scoring system performed well with respect to predicting morbidity risk following gastric cancer resection. For predicting postoperative mortality, p-POSSUM and o-POSSUM exhibited superior performance relative to POSSUM and APACHE II