ObjectiveTo understand patients’ cognition of third-party mediation model for medical disputes, analyze the factors influencing the trust of patients on third-party mediation, and propose recommendations for building third-party mediation mechanisms. MethodsFrom November 2013 to April 2014, we referred past literature to design a relevant questionnaire on the cognition of third-party mediation for medical disputes. Patients who had reached the end of the treatment were surveyed by random cluster sampling. The raw data were put into the computer for statistical analysis by SPSS 18.0. ResultsAfter giving out 500 questionnaires, we acquired 486 effective questionnaires. The result showed that 61.52% of the patients knew of third-party mediation; 55.35% of the patients considered that thirdparty mediation should be set in and supervised by the court or judicial administrative department; if the mediation failed, 57.41% of the patients chose to resolve the dispute through legal channels, and 67.90% of the patients tended to confirm the force of mediation conclusion by arbitration; 70.58% of the patients considered that mediators should have professional background of medicine and law; 73.05% of the patients tended to take conclusions of forensic identification as the basis for mediation; 64.81% of the patients were biased to take Tort Liability Act as the basis for determining the compensation; 53.70% of the patients believed that financial allocations could solve the fund problems of third-party mediation, while 38.48% of the patients thought the funds should be provided by insurance companies; 91.15% of the patients thought the medical institutions should purchase medical liability insurance, and 54.32% of the patients thought insurance companies should not intervene the process of meditation. Conclusions Government should provide financial allocations to ensure the funds of third-party mediation. Besides, medical insurance should be brought in as a supplement. Medical institutions should purchase medical liability insurance to solve problems caused by medical disputes. Third-party mediation should be set in and supervised by the court or the judicial administrative department. Mediators should have professional background of medicine and law. Conclusions of forensic identification should be the basis for third-party mediation.
Objective To improve hand hygiene executive ability of healthcare workers in medical institutions in Anhui Province by multi-modal interventions with the administrative intervention as the guide. Methods The PDCA management mode was adopted in a step-by-step implementation of plan, implementation, inspection, improvement, and effectiveness evaluation in Anhui Province from April 2014 to December 2016. The management indicators of hand hygiene before and after the intervention in 1 353 hospitals were investigated and evaluated. Results The overall evaluation of the hand hygiene at the end of the implemention showed that 85.29% (58/68) of the tertiary hospitals, 84.07% (227/270) of the second-class hospitals and 66.63% (595/893) of the primary-level hospitals had well-equipped hand hygiene facilities. About 92.65% (63/68) of the tertiary hospitals, 100.00% (270/270) of the second-class hospitals and 50.06% (447/893) of the primary-level hospitals had staff training of hand hygiene knowledge. The compliance of hand hygiene before and after intervention increased from 36.68% to 61.93%, the correct rate of hand washing increased from 37.60% to 89.28%, the awareness rate of related knowledge increased from 41.20% to 86.07%, and the dosage of hand disinfectant increased from 2.59 mL to 7.10 mL. Conclusion To take multi-model interventions with the administrative intervention as the guide, can effectively improve the quality of hand hygiene management and the executive force.
At present the prediction method of epilepsy patients is very time-consuming and vulnerable to subjective factors, so this paper presented an automatic recognition method of epilepsy electroencephalogram (EEG) based on common spatial model (CSP) and support vector machine (SVM). In this method, the CSP algorithm for extracting spatial characteristics was applied to the detection of epileptic EEG signals. However, the algorithm did not consider the nonlinear dynamic characteristics of the signals and ignored the time-frequency information, so the complementary characteristics of standard deviation, entropy and wavelet packet energy were selected for the combination in the feature extraction stage. The classification process adopted a new double classification model based on SVM. First, the normal, interictal and ictal periods were divided into normal and paroxysmal periods (including interictal and ictal periods), and then the samples belonging to the paroxysmal periods were classified into interictal and ictal periods. Finally, three categories of recognition were realized. The experimental data came from the epilepsy study at the University of Bonn in Germany. The average recognition rate was 98.73% in the first category and 99.90% in the second category. The experimental results show that the introduction of spatial characteristics and double classification model can effectively solve the problem of low recognition rate between interictal and ictal periods in many literatures, and improve the identification efficiency of each period, so it provides an effective detecting means for the prediction of epilepsy.
Signal classification is a key of brain-computer interface (BCI). In this paper, we present a new method for classifying the electroencephalogram (EEG) signals of which the features are heterogeneous. This method is called wrapped elastic net feature selection and classification. Firstly, we used the joint application of time-domain statistic, power spectral density (PSD), common spatial pattern (CSP) and autoregressive (AR) model to extract high-dimensional fused features of the preprocessed EEG signals. Then we used the wrapped method for feature selection. We fitted the logistic regression model penalized with elastic net on the training data, and obtained the parameter estimation by coordinate descent method. Then we selected best feature subset by using 10-fold cross-validation. Finally, we classified the test sample using the trained model. Data used in the experiment were the EEG data from international BCI Competition Ⅳ. The results showed that the method proposed was suitable for fused feature selection with high-dimension. For identifying EEG signals, it is more effective and faster, and can single out a more relevant subset to obtain a relatively simple model. The average test accuracy reached 81.78%.
Central serous chorioretinitis (CSC) is a kind of choroidal retinopathy characterized by choroidal vasodilatation and hyperpermeability, retinal pigment epithelial cell lesions and serous retinal detachment. Various imaging examinations and imaging techniques have been used to describe the characteristics of the retina and choroid. Fundus manifestations of different types of CSC has both generality, and have their respective characteristic. The classification of CSC and its differentiation from other diseases including the choroidal neovascularization and pachychoroidopathy spectrum depending on varieties of fundus imaging techniques. The current study aims to review the various performance characteristics of CSC especially for chronic CSC with multimodal imaging and the current research progress, so as to provide reference for ophthalmologists to more comprehensively and intuitively understand the clinical characteristics and potential pathogenesis of CSC, and also to provide basis for multimodal imaging assisted diagnosis and treatment.
Within the context of the "Healthy China Strategy" and the "Biology-Psychology-Society" medical model, the goals, content and methods of medical education have undergone tremendous changes. To keep up with the pace of development of medical technology and medical concepts, medical education requires major reforms, and medical teaching models requires reconstruction. Based on previous investigations and discussions and considering the West China medical education as an example, this paper summarizes the difficulties that will be faced in the transformation and reform of modern medical education and discusses and analyzes the future direction of medical education reform.
ObjectiveTo develop Knowledge attitude behavior and practice (KABP) health education path table, and to explore its application in health education of physician-nurse collaboration for children with epilepsy, and provide practical reference for health education of children with epilepsy.MethodsA convenient sampling method was used to select 94 family units of children with epilepsy and their main caregivers from the Department of Neurology in Hunan Children’s Hospital from September 2018 to March 2019. Divided into observation group and control group, 47 cases in each group. In the control group, the health care education was carried out by the conventional method of medical personnel’s one-way input of knowledge. The observation group conducted health education through interactive participation in the path of the health education path of KABP on the basis of regular health education. Then compared the effect of the health education between the two groups.ResultsAfter the intervention, the quality of life scores of the observation group were significantly higher than the control group (P<0.01). The relevant knowledge scores of main caregivers at 1 and 3 months after discharge were significant higher than those in the control group (P=0.008, P=0.001). The medication compliance scores of children with epilepsy at 1 and 3 months after discharge were significant higher than those in the control group (P=0.010, P=0.006).ConclusionsThe KABP health education pathway can improve the knowledge level of caregivers, as well as the medication compliance and quality of life of children with epilepsy.
Brain-computer interface (BCI) provides a direct communicating and controlling approach between the brain and surrounding environment, which attracts a wide range of interest in the fields of brain science and artificial intelligence. It is a core to decode the electroencephalogram (EEG) feature in the BCI system. The decoding efficiency highly depends on the feature extraction and feature classification algorithms. In this paper, we first introduce the commonly-used EEG features in the BCI system. Then we introduce the basic classical algorithms and their advanced versions used in the BCI system. Finally, we present some new BCI algorithms proposed in recent years. We hope this paper can spark fresh thinking for the research and development of high-performance BCI system.
Automated characterization of different vessel wall tissues including atherosclerotic plaques, branchings and stents from intravascular ultrasound (IVUS) gray-scale images was addressed. The texture features of each frame were firstly detected with local binary pattern (LBP), Haar-like and Gabor filter in the present study. Then, a Gentle Adaboost classifier was designed to classify tissue features. The methods were validated with clinically acquired image data. The manual characterization results obtained by experienced physicians were adopted as the golden standard to evaluate the accuracy. Results indicated that the recognition accuracy of lipidic plaques reached 94.54%, while classification precision of fibrous and calcified plaques reached 93.08%. High recognition accuracy can be reached up to branchings 93.20% and stents 93.50%, respectively.
Temporal lobe epilepsy is the most common type of epilepsy in clinic. In recent years, many studies have found that patients with temporal lobe epilepsy have different degrees of influence in executive function related fields. This influence may not only exist in a certain field of executive function, but may be affected in several fields, and may be related to the origin site of seizures. However, up to now, there is no unified standard for the composition of executive function, and it is widely accepted that the three core components of executive function are working memory, inhibitory control and cognitive flexibility/switching. In addition, the International League Against Epilepsy proposed a new definition in 2010, and epilepsy is a brain network disease. There is a close relationship between brain neural network and cognitive impairment. According to the cognitive field, the brain neural network can be divided into six types: default mode network, salience network, executive control network, dorsal attention network, somatic motor network and visual network. In recent years, there has been increasing evidence that four related internal brain networks are series in a range of cognitive processes. The executive dysfunction of temporal lobe epilepsy may be related to the changes of functional connectivity of neural network, and may be related to the left uncinate fasciculus. This article reviews the research progress related to executive function in temporal lobe epilepsy from working memory, inhibitory control and cognitive flexibility, and discusses the correlation between the changes of temporal lobe epilepsy neural network and executive function research.