Assessment of financial risk exposure of county-level public hospitals reform pilots in Inner Mongolia Autonomous Region / 中华医院管理杂志

Objective To establish an assessment model of financial risk exposure for the county-levelpublic hospitals in Inner Mongolia Autonomous Region,which can be used to assess the risk exposure of the hospital in question,and as decision making reference for their financial management and risk prevention and control.Methods Using indicators standardized methods and entropy method to process 1 5 financial indicators (quantitative indicators and qualitative indicators )for the 20 public hospitals,and using the gray clustering method to assess financial risk exposure.Results 70% of the county-level public hospitals are faced with less financial risks,while four of them need to pay close attention,and two have large loopholes pending solution.Conclusions Entropy-Gray clustering methods can complement each other,as found in the study.This study proves its significance,and health authorities should establish their long-term financial risk control mechanisms.

Automatic Classification of Epileptic Electroencephalogram Signal Based on Improved Multivariate Multiscale Entropy / 生物医学工程学杂志

Traditional sample entropy fails to quantify inherent long-range dependencies among real data. Multiscale sample entropy (MSE) can detect intrinsic correlations in data, but it is usually used in univariate data. To generalize this method for multichannel data, we introduced multivariate multiscale entropy into multiscale signals as a reflection of the nonlinear dynamic correlation. But traditional multivariate multiscale entropy has a large quantity of computation and costs a large period of time and space for more channel system, so that it can not reflect the correlation between variables timely and accurately. In this paper, therefore, an improved multivariate multiscale entropy embeds on all variables at the same time, instead of embedding on a single variable as in the traditional methods, to solve the memory overflow while the number of channels rise, and it is more suitable for the actual multivariate signal analysis. The method was tested in simulation data and Bonn epilepsy dataset. The simulation results showed that the proposed method had a good performance to distinguish correlation data. Bonn epilepsy dataset experiment also showed that the method had a better classification accuracy among the five data set, especially with an accuracy of 100% for data collection of Z and S.