To solve the defect which is recognizing but not rating the stress, or rating but not considering the influence of the previous stress state to the current state of the existing affective stress evaluation method, this paper proposes an approach of affective stress rating model on electrocardiogram (ECG). An affective stress rating algorithm based on hidden Markov model (HMM) was established with the theory of affective computing. The individual's affective stress was rated using this affective rating model combining the investigation questionnaire. Features like complexity and approximate entropy of ECG were used in the model, and a matching process suggested that it improved the accuracy of affective stress rating. The result of the experiment illustrated that the model considering the environmental factors and the influence of previous stress state to the current state was an effective method in affective stress rating, and the accuracy of rating was improved by this affective stress rating method.
Objective To evaluate the cost effectiveness of human papillomavirus vaccine (HPV) for treating cervical cancer. Methods We constructed a Markov model to evaluate the cost-effectiveness of HPV versus Chinese healthy women aged 18 to 25 for treating Cervical Cancer. We calculated the clinical benefits and cost-effectiveness and judged the results based on willing to pay. Sensitivity analysis was made for parameters like cost, discounting rate and vaccine efficacy. Results HPV vaccination was a cost-effective option under the local willing to pay value with the incremental cost utility ratio 43 489 per QALY gained. It proved that vaccination was an economic and effective solution. Conclusion Given the results of Markov model, the cost effectiveness of HPV vaccination of Chinese women aged 18 to 25 is positive. Considering the data sources and model hypothesis, this report has some limitations. Further studies are warranted.
In current domestic research on laparoscopic training, researchers usually consider instrument movement path in the hand-eye coordination relationship. However, they ignore the information contained in visual cues by which could guide and control instrument movements. Studies in other areas have shown that trainers can improve their perceptual-motor skills by gaze training. This paper was designed to examine the effectiveness of eye gaze tracking technology in laparoscopic training and to analyze gaze strategy of the subjects in different training methods. The Tobii X1 Light Eye Tracker was used to track the gaze position of subjects when they were performing the two-handed transferring task in box trainer, and to obtain parameters related to gaze strategy including the efficiency of task completion, as well as visual search, visual processing and observation transfer analysis based on Markov chain model. The results showed that the completion time during the last training in gaze training group was decreased by 101.5 s comparing to the first training. Compared with video training group, gaze strategy of gaze training group has a significant change, such as fixation and saccade duration rate was increased by 38%, fixation duration on target area was increased, and saccade amplitude increased by 0.58°, and the probability of the fixation point transferring to equipment decreased by 15%. The results demonstrated that eye gaze tracking technology can be used in laparoscopic training, and can improve the subjects’ skills and shorten the learning curve by learning gaze strategies of experts.
Health economics analysis has become increasingly important in recent years. It is essential to master the use of relevant software to conduct research in health economics. TreeAge Pro software is widely used in the healthcare decision analysis. It can carry out decision analysis, cost-effectiveness analysis, and Monte Carlo simulation. With powerful functionlity and outstanding visualization, it can build Markov disease transition models to analyze Markov processes according to disease models and accomplish decision analysis with decision trees and influence diagrams. This paper introduces cost-effectiveness analysis based on Markov model with examples and explains the main graphs.
Sleep status is an important indicator to evaluate the health status of human beings. In this paper, we proposed a novel type of unperturbed sleep monitoring system under pillow to identify the pattern change of heart rate variability (HRV) through obtained RR interval signal, and to calculate the corresponding sleep stages combined with hidden Markov model (HMM) under the no-perception condition. In order to solve the existing problems of sleep staging based on HMM, ensemble empirical mode decomposition (EEMD) was proposed to eliminate the error caused by the individual differences in HRV and then to calculate the corresponding sleep stages. Ten normal subjects of different age and gender without sleep disorders were selected from Guangzhou Institute of Respirator Diseases for heart rate monitoring. Comparing sleep stage results based on HMM to that of polysomnography (PSG), the experimental results validate that the proposed noninvasive monitoring system can capture the sleep stages S1–S4 with an accuracy more than 60%, and performs superior to that of the existing sleep staging scheme based on HMM.
ObjectivesTo compare the efficacy and economy of febuxostat and allopurinol in the treatment of chronic gout, and to provide reference for clinical rational drug use.MethodsThe Markov model was established to conduct cost-effectiveness analysis for febuxostat and allopurinol serving as the front-line treated medicines. In view of the uncertainty of model parameters, single factor, probability sensitivity analysis and other methods were used to analyze the stability of the results.ResultsThe cost of the therapeutic schedule of allopurinol 300 mg was lower than febuxostat 40 mg, and it saved RMB 4 339.6 Yuan for each patients on average, while obtained 0.067 more QALY. Uncertainty analysis revealed that only those utility value which could not reach the standard influenced the final results in all included variable elements. When the aspiration payment value was zero, the percentage of therapeutic schedule for allopurinol 300 mg was 100. With the increase of aspiration payment value, the probability for febuxostat scheme becoming the superior one showed a very gradual growth. When the aspiration payment value reached 150 000, the probability still remained under 10%.ConclusionsAllopurinol is more economical than finasteride as the first choice in the treatment of chronic gout. Therefore, it is recommended that allopurinol should be used as the first-line drug for economical considerations.
Rapid and accurate recognition of human action and road condition is a foundation and precondition of implementing self-control of intelligent prosthesis. In this paper, a Gaussian mixture model and hidden Markov model are used to recognize the road condition and human motion modes based on the inertial sensor in artificial limb (lower limb). Firstly, the inertial sensor is used to collect the acceleration, angle and angular velocity signals in the direction of x, y and z axes of lower limbs. Then we intercept the signal segment with the time window and eliminate the noise by wavelet packet transform, and the fast Fourier transform is used to extract the features of motion. Then the principal component analysis (PCA) is carried out to remove redundant information of the features. Finally, Gaussian mixture model and hidden Markov model are used to identify the human motion modes and road condition. The experimental results show that the recognition rate of routine movement (walking, running, riding, uphill, downhill, up stairs and down stairs) is 96.25%, 92.5%, 96.25%, 91.25%, 93.75%, 88.75% and 90% respectively. Compared with the support vector machine (SVM) method, the results show that the recognition rate of our proposed method is obviously higher, and it can provide a new way for the monitoring and control of the intelligent prosthesis in the future.
Markov model is one of the decision analysis models, which is widely used in pharmacoeconomic evaluation studies. In terms of dealing with changes of disease risks during different times, the transition probabilities among different Markov health states becomes hard to calculate. Nevertheless, survival analysis is an available resolution. In this paper, we introduced how to apply survival analysis in calculation of transition probability in time-dependent model based on cumulative probability with a case analysis on advanced gastric cancer Markov model, and provide more information for researchers to build models.
Objectives To determine the health benefit of elbasvir/grazoprevir versus peginterferon combing with ribavirin (PR regimen) for Chinese chronic hepatitis C patients with genotype 1b infection. Methods Markov cohort state-transition models were constructed to conduct cost utility analysis. Sensitivity analyses were performed based on base-case analysis. Results Elbasvir/grazoprevir was dominant versus PR, resulting in higher QALYs and lower costs for both noncirrhotic patients (13.867 5 QALYs, 82 090.82 RMB vs. 12.696 2 QALYs, 122 791.55 RMB) and cirrhotic patients (12.841 6 QALYs, 225 807.70 RMB vs. 8.892 4 QALYs, 326 545.01 RMB). Elbasvir/grazoprevir was economically dominant in nearly 100% among all patients within the range of threshold from 0 to 161 805 RMB/QALY. Conclusions Elbasvir/grazoprevir was dominant in treatment of genotype 1b chronic hepatitis C infection in China.
In order to meet the requirements in the cooperation and competition experiments for an individual patient in clinical application, two human interactive behavior key-press models based on hidden Markov model (HMM) were proposed. To validate the cooperative and competitive models, a verification experimental task was designed and the data were collected. The correlation of the score and subjects’ participation level has been used to analyze the reasonability verification. Behavior verification was conducted by comparing the statistical difference in response time for subjects between human-human and human-computer experiment. In order to verify the physiological validity of the models, we have utilized the coherence analysis to analyze the deep information of prefrontal brain area. Reasonability verification shows that the correlation coefficient for the training data and the testing data is 0.883 1 and 0.578 6 respectively based on cooperation model, and 0.813 1 and 0.617 8 respectively based on the competition model. The behavioral verification result shows that the cooperation and competition models have an accuracy of 71.43% respectively. The results of physiological validity show that the deep information of prefrontal brain area could been extracted based on the cooperation and competition models, and reveal the consistency of coherence between the double key-press cooperative and competitive experiments, respectively. Above all, the high consistency is obtained between the cooperatio/competition model and the double key-press experiment by the behavioral and physiological evaluation results. Consequently, the cooperation and competition models could be applied to clinical trials.