Electromagnetic stimulation is an important neuromodulation technique that modulates the electrical activity of neurons and affects cortical excitability for the purpose of modulating the nervous system. The phenomenon of inverse stochastic resonance is a response mechanism of the biological nervous system to external signals and plays an important role in the signal processing of the nervous system. In this paper, a small-world neural network with electrical synaptic connections was constructed, and the inverse stochastic resonance of the small-world neural network under electromagnetic stimulation was investigated by analyzing the dynamics of the neural network. The results showed that: the Levy channel noise under electromagnetic stimulation could cause the occurrence of inverse stochastic resonance in small-world neural networks; the characteristic index and location parameter of the noise had significant effects on the intensity and duration of the inverse stochastic resonance in neural networks; the larger the probability of randomly adding edges and the number of nearest neighbor nodes in small-world networks, the more favorable the anti-stochastic resonance was; by adjusting the electromagnetic stimulation parameters, a dual regulation of the inverse stochastic resonance of the neural network can be achieved. The results of this study provide some theoretical support for exploring the regulation mechanism of electromagnetic nerve stimulation technology and the signal processing mechanism of nervous system.
Action Potentials/physiology* ; Computer Simulation ; Models, Neurological ; Stochastic Processes ; Neurons/physiology* ; Electromagnetic Phenomena

Vision is an important way for human beings to interact with the outside world and obtain information. In order to research human visual behavior under different conditions, this paper uses a Gaussian mixture-hidden Markov model (GMM-HMM) to model the scanpath, and proposes a new model optimization method, time-shifting segmentation (TSS). The TSS method can highlight the characteristics of the time dimension in the scanpath, improve the pattern recognition results, and enhance the stability of the model. In this paper, a linear discriminant analysis (LDA) method is used for multi-dimensional feature pattern recognition to evaluates the rationality and the accuracy of the proposed model. Four sets of comparative trials were carried out for the model evaluation. The first group applied the GMM-HMM to model the scanpath, and the average accuracy of the classification could reach 0.507, which is greater than the opportunity probability of three classification (0.333). The second set of trial applied TSS method, and the mean accuracy of classification was raised to 0.610. The third group combined GMM-HMM with TSS method, and the mean accuracy of classification reached 0.602, which was more stable than the second model. Finally, comparing the model analysis results with the saccade amplitude (SA) characteristics analysis results, the modeling analysis method is much better than the basic information analysis method. Via analyzing the characteristics of three types of tasks, the results show that the free viewing task have higher specificity value and a higher sensitivity to the cued object search task. In summary, the application of GMM-HMM model has a good performance in scanpath pattern recognition, and the introduction of TSS method can enhance the difference of scanpath characteristics. Especially for the recognition of the scanpath of search-type tasks, the model has better advantages. And it also provides a new solution for a single state eye movement sequence.
Algorithms ; Discriminant Analysis ; Eye Movements ; Humans ; Markov Chains ; Normal Distribution ; Probability

Heart sound segmentation is a key step before heart sound classification. It refers to the processing of the acquired heart sound signal that separates the cardiac cycle into systolic and diastolic, etc. To solve the accuracy limitation of heart sound segmentation without relying on electrocardiogram, an algorithm based on the duration hidden Markov model (DHMM) was proposed. Firstly, the heart sound samples were positionally labeled. Then autocorrelation estimation method was used to estimate cardiac cycle duration, and Gaussian mixture distribution was used to model the duration of sample-state. Next, the hidden Markov model (HMM) was optimized in the training set and the DHMM was established. Finally, the Viterbi algorithm was used to track back the state of heart sounds to obtain S
Algorithms ; Electrocardiography ; Heart Sounds ; Markov Chains ; Normal Distribution

PURPOSE: Although the economic and mortality burden of atrial fibrillation (AF) is substantial, it remains unclear which treatment strategies for rate and rhythm control are most cost-effective. Consequently, economic factors can play an adjunctive role in guiding treatment selection. MATERIALS AND METHODS: We built a Markov chain Monte Carlo model using the Korean Health Insurance Review & Assessment Service database. Drugs for rate control and rhythm control in AF were analyzed. Cost-effective therapies were selected using a cost-effectiveness ratio, calculated by net cost and quality-adjusted life years (QALY). RESULTS: In the National Health Insurance Service data, 268149 patients with prevalent AF (age ≥18 years) were identified between January 1, 2013 and December 31, 2015. Among them, 212459 and 55690 patients were taking drugs for rate and rhythm control, respectively. Atenolol cost $714/QALY. Among the rate-control medications, the cost of propranolol was lowest at $487/QALY, while that of carvedilol was highest at $1363/QALY. Among the rhythm-control medications, the cost of pilsicainide was lowest at $638/QALY, while that of amiodarone was highest at $986/QALY. Flecainide and propafenone cost $834 and $830/QALY, respectively. The cost-effectiveness threshold of all drugs was lower than $30000/QALY. Compared with atenolol, the rate-control drugs propranolol, betaxolol, bevantolol, bisoprolol, diltiazem, and verapamil, as well as the rhythm-control drugs sotalol, pilsicainide, flecainide, propafenone, and dronedarone, showed better incremental cost-effectiveness ratios. CONCLUSION: Propranolol and pilsicainide appear to be cost-effective in patients with AF in Korea assuming that drug usage or compliance is the same.
Amiodarone ; Atenolol ; Atrial Fibrillation ; Betaxolol ; Bisoprolol ; Compliance ; Cost-Benefit Analysis ; Diltiazem ; Flecainide ; Humans ; Insurance, Health ; Korea ; Markov Chains ; Mortality ; National Health Programs ; Propafenone ; Propranolol ; Quality-Adjusted Life Years ; Sotalol ; Verapamil

The objective of this study is to describe the general approaches to network meta-analysis that are available for quantitative data synthesis using R software. We conducted a network meta-analysis using two approaches: Bayesian and frequentist methods. The corresponding R packages were “gemtc” for the Bayesian approach and “netmeta” for the frequentist approach. In estimating a network meta-analysis model using a Bayesian framework, the “rjags” package is a common tool. “rjags” implements Markov chain Monte Carlo simulation with a graphical output. The estimated overall effect sizes, test for heterogeneity, moderator effects, and publication bias were reported using R software. The authors focus on two flexible models, Bayesian and frequentist, to determine overall effect sizes in network meta-analysis. This study focused on the practical methods of network meta-analysis rather than theoretical concepts, making the material easy to understand for Korean researchers who did not major in statistics. The authors hope that this study will help many Korean researchers to perform network meta-analyses and conduct related research more easily with R software.
Bayes Theorem ; Hope ; Markov Chains ; Population Characteristics ; Publication Bias

The objective of this study is to describe the general approaches to network meta-analysis that are available for quantitative data synthesis using R software. We conducted a network meta-analysis using two approaches: Bayesian and frequentist methods. The corresponding R packages were “gemtc” for the Bayesian approach and “netmeta” for the frequentist approach. In estimating a network meta-analysis model using a Bayesian framework, the “rjags” package is a common tool. “rjags” implements Markov chain Monte Carlo simulation with a graphical output. The estimated overall effect sizes, test for heterogeneity, moderator effects, and publication bias were reported using R software. The authors focus on two flexible models, Bayesian and frequentist, to determine overall effect sizes in network meta-analysis. This study focused on the practical methods of network meta-analysis rather than theoretical concepts, making the material easy to understand for Korean researchers who did not major in statistics. The authors hope that this study will help many Korean researchers to perform network meta-analyses and conduct related research more easily with R software.
Bayes Theorem ; Hope ; Markov Chains ; Population Characteristics ; Publication Bias

Neurocognitive deficits are frequently observed in patients with schizophrenia and major depressive disorder (MDD). The relations between cognitive features may be represented by neurocognitive graphs based on cognitive features, modeled as Gaussian Markov random fields. However, it is unclear whether it is possible to differentiate between phenotypic patterns associated with the differential diagnosis of schizophrenia and depression using this neurocognitive graph approach. In this study, we enrolled 215 first-episode patients with schizophrenia (FES), 125 with MDD, and 237 demographically-matched healthy controls (HCs). The cognitive performance of all participants was evaluated using a battery of neurocognitive tests. The graphical LASSO model was trained with a one-vs-one scenario to learn the conditional independent structure of neurocognitive features of each group. Participants in the holdout dataset were classified into different groups with the highest likelihood. A partial correlation matrix was transformed from the graphical model to further explore the neurocognitive graph for each group. The classification approach identified the diagnostic class for individuals with an average accuracy of 73.41% for FES vs HC, 67.07% for MDD vs HC, and 59.48% for FES vs MDD. Both of the neurocognitive graphs for FES and MDD had more connections and higher node centrality than those for HC. The neurocognitive graph for FES was less sparse and had more connections than that for MDD. Thus, neurocognitive graphs based on cognitive features are promising for describing endophenotypes that may discriminate schizophrenia from depression.
Adult ; Algorithms ; Depressive Disorder, Major ; classification ; diagnosis ; Endophenotypes ; analysis ; Female ; Humans ; Machine Learning ; Male ; Markov Chains ; Neuropsychological Tests ; Schizophrenia ; classification ; diagnosis ; Young Adult

The non-coding DNA in eukaryotic genomes encodes a language which programs chromatin accessibility, transcription factor binding, and various other activities. The objective of this short report was to determine the impact of primary DNA sequence on the epigenomic landscape across 200-base pair genomic units by integrating nine publicly available ChromHMM Browser Extensible Data files of the Encyclopedia of DNA Elements (ENCODE) project. The nucleotide frequency profiles of nine chromatin annotations with the units of 200 bp were analyzed and integrative Markov chains were built to detect the Markov properties of the DNA sequences in some of the active chromatin states of different ChromHMM regions. Our aim was to identify the possible relationship between DNA sequences and the newly built chromatin states based on the integrated ChromHMM datasets of different cells and tissue types.
Base Sequence* ; Chromatin ; Dataset ; DNA* ; Epigenomics ; Genome ; Information Storage and Retrieval ; Markov Chains ; Transcription Factors

OBJECTIVE: To evaluate the cost-effectiveness of nodal staging surgery before chemoradiotherapy (CRT) for locally advanced cervical cancer in the era of positron emission tomography/computed tomography (PET/CT). METHODS: A modified Markov model was constructed to evaluate the cost-effectiveness of para-aortic staging surgery before definite CRT when no uptake is recorded in the para-aortic lymph nodes (PALN) on PET/CT. Survival and complication rates were estimated based on the published literature. Cost data were obtained from the Korean Health Insurance Review and Assessment Service. Strategies were compared using an incremental cost-effectiveness ratio (ICER). Sensitivity analyses were performed, including estimates for the performance of PET/CT, postoperative complication rate, and varying survival rates according to the radiation field. RESULTS: We compared two strategies: strategy 1, pelvic CRT for all patients; and strategy 2, nodal staging surgery followed by extended-field CRT when PALN metastasis was found and pelvic CRT otherwise. The ICER for strategy 2 compared to strategy 1 was $19,505 per quality-adjusted life year (QALY). Under deterministic sensitivity analyses, the model was relatively sensitive to survival reduction in patients who undergo pelvic CRT alone despite having occult PALN metastasis. A probabilistic sensitivity analysis demonstrated the robustness of the case results, with a 91% probability of cost-effectiveness at the willingness-to-pay thresholds of $60,000/QALY. CONCLUSION: Nodal staging surgery before definite CRT may be cost-effective when PET/CT imaging shows no evidence of PALN metastasis. Prospective trials are warranted to transfer these results to guidelines.
Chemoradiotherapy/*economics ; Combined Modality Therapy/economics ; Cost-Benefit Analysis ; Female ; Humans ; Laparoscopy/economics ; Lymph Node Excision/*economics/methods ; Lymphatic Metastasis ; Markov Chains ; Multimodal Imaging/economics ; Neoplasm Staging ; Positron-Emission Tomography/economics ; Quality of Life ; Quality-Adjusted Life Years ; Tomography, X-Ray Computed/economics ; Uterine Cervical Neoplasms/*economics/therapy

BACKGROUNDTertiary hospitals serve as the medical service center within the region and play an important role in the medical and health service system. They are also the key targets of public hospital reform in the new era in China. Through the reform of health system, the public hospital efficiency has changed remarkably. Therefore, this study aimed to provide some advice for efficiency assessment of public hospitals in China by comparing and analyzing the consistency of results obtained by three commonly used methods for examining hospital efficiency, that is, ratio analysis (RA), stochastic frontier analysis (SFA), and data envelopment analysis (DEA).

METHODSThe theoretical basis, operational processes, and the application status of RA, SFA, and DEA were learned through literature analysis. Then, the empirical analysis was conducted based on measured data from 51 tertiary public hospitals in Beijing from 2009 to 2011.

RESULTSThe average values of hospital efficiency calculated by SFA with index screening and principal component analysis (PCA) results and those calculated by DEA with index screening results were relatively stable. The efficiency of specialized hospitals was higher than that of general hospitals and that of traditional Chinese medicine hospitals. The results obtained by SFA with index screening results and the results obtained by SFA with PCA results showed a relatively high correlation (r-value in 2009, 2010, and 2011 were 0.869, 0.753, and 0.842, respectively, P < 0.01). The correlation between results obtained by DEA with index screening results and PCA results and results obtained by other methods showed statistical significance, but the correlation between results obtained by DEA with index screening results and PCA results was lower than that between results obtained by SFA with index screening results and PCA results.

CONCLUSIONSRA is not suitable for multi-index evaluation of hospital efficiency. In the given conditions, SFA is a stable efficiency analysis method. In the evaluation of hospital efficiency, DEA combined with PCA should be adopted with caution due to its poor stability.