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

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

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 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

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

This study involved a cost-utility analysis of early diagnosis and treatment of diabetic retinopathy depending on the screening strategy used. The four screening strategies evaluated were no screening, opportunistic examination, systematic fundus photography, and systematic examination by an ophthalmologists. Each strategy was evaluated in 10,000 adults aged 40 yr with newly diagnosed diabetes mellitus (hypothetical cohort). The cost of each strategy was estimated in the perspective of both payer and health care system. The utility was estimated using quality-adjusted life years (QALY). Incremental Cost Effectiveness Ratio (ICER) for the different screening strategies was analyzed. After exclusion of the weakly dominating opportunistic strategy, the ICER of systematic photography was 57,716,867 and that of systematic examination by ophthalmologists was 419,989,046 from the perspective of the healthcare system. According to the results, the systematic strategy is preferable to the opportunistic strategy from the perspective of both a payer and a healthcare system. Although systematic examination by ophthalmologists may have higher utility than systematic photography, it is associated with higher cost. The systematic photography is the best strategy in terms of cost-utility. However systematic examination by ophthalmologists can also be a suitable policy alternative, if the incremental cost is socially acceptable.
Adult ; Aged ; Aged, 80 and over ; *Cost-Benefit Analysis ; Diabetic Retinopathy/*diagnosis/economics/*therapy ; Diagnostic Techniques, Ophthalmological/economics ; Early Diagnosis ; Female ; Fluorescein Angiography/economics ; Health Care Costs ; Humans ; Male ; Markov Chains ; Mass Screening/*economics/methods/statistics & numerical data ; Middle Aged ; Models, Economic ; National Health Programs/economics ; Quality-Adjusted Life Years ; Republic of Korea

OBJECTIVETo estimate the cost-effectiveness of esophageal cancer endoscopic screening once-in-a-lifetime and to predict the optimal screening age for people in high-risk areas of rural China.

METHODSA Markov model was constructed to predict and compare the effect of four esophageal cancer endoscopic screening modalities which varied with different screening ages. Long-term epidemiological effectiveness and cost-effectiveness were predicted by simulation of the model.

RESULTSCompared with the control group, strategies starting at 40, 45, 50 and 55 year-old had saved life-years of 629.51, 769.88, 738.98 and 533.21 years per 100 000 people, respectively, of which the strategy starting at 45 year-old saved the maximum life years. All strategies were cost-effective and starting at 40 year-old cost the most per life-year saved. Among all alternatives, strategies starting age at 45 year-old and 50 year-old were incremental cost-effective, and the incremental cost-effective ratios were 34 962.87 and 3 346.43 RMB per life year saved, respectively.

CONCLUSIONSThe strategy starting at 40 year-old implemented at present and other strategies were cost-effective in high-risk areas of rural China. However, the 45-year-old group is more aligned with the principle of cost-effectiveness. Considering the cost-effectiveness of different strategies and social economic status, 45 year-old is regarded as the optimal starting age of esophageal cancer once-in-a-lifetime endoscopic screening and is recommended in areas lacking health resources. The strategy of starting age at 40 year-old which could obtain better screening effects would be preferable in wealthy regions.

Adult ; Age Factors ; Case-Control Studies ; China ; Cost-Benefit Analysis ; Early Detection of Cancer ; Esophageal Neoplasms ; diagnosis ; Esophagoscopy ; economics ; Humans ; Markov Chains ; Middle Aged ; Rural Population

Cough recognition provides important clinical information for the treatment of many respiratory diseases. A new Mel frequency cepstrum coefficient (MFCC) extracting method has been proposed on the basis of the distributional characteristics of cough spectrum. The whole frequency band was divided into several sub-bands, and the energy coefficient for each band was obtained by method of principle component analysis. Then non-uniform filter-bank in Mel frequency is designed to improve the extracting process of MFCC by distributing filters according to the spectrum energy coefficients. Cough recognition experiment using hidden Markov model was carried out, and the results
Cough ; Humans ; Markov Chains ; Principal Component Analysis ; Respiratory Tract Diseases ; diagnosis ; Sound