Detection of the quality of vital signals by the Monte Carlo Markov Chain (MCMC) method and noise deleting

Vajargah, Kianoush Fathi; Benis, Sara Ghaniyari; Golshan, Hamid Mottaghi

doi:10.1007/s13755-021-00157-5

Cited by 3 publications

(3 citation statements)

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“… 37–40 Other studies that have attempted to quantify the variability present in Markov chain predictions focus on the transition matrix, with each transition probability having a confidence interval 41 . However, since the matrix generated 10 subsequent predictions, small initial errors would be compounded 42–45 . For example, a 5% error would then be compounded to a 62.8% error after 10 iterations.…”

Section: Discussionmentioning

confidence: 99%

“… 41 However, since the matrix generated 10 subsequent predictions, small initial errors would be compounded. 42 , 43 , 44 , 45 For example, a 5% error would then be compounded to a 62.8% error after 10 iterations. The analysis utilized bootstrap simulation to generate 10,000 unique Markov chains from the data and generated accurate confidence intervals from these 10,000 generations, allowing us to have confidence of accurately estimating the true variance and considering the compounding error that is present from each subsequent projection into the future.…”

Section: Discussionmentioning

confidence: 99%

“…41 However, since the matrix generated 10 subsequent predictions, small initial errors would be compounded. [42][43][44][45] For example, a 5% error would then be compounded to a 62.8%…”

Section: Gained or Maintained Weigh P-valuementioning

confidence: 99%

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Stochastic modeling of obesity status in United States adults using Markov Chains: A nationally representative analysis of population health data from 2017–2020

Huang

2023

Obesity Science & Practice

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ImportanceThe prevalence of obesity among United States adults has increased from 34.9% in 2013–2014 to 42.8% in 2017–2018. Developing methods to model the increase of obesity over‐time is a necessity to know how to accurately quantify its cost and to develop solutions to combat this national public health emergency.MethodsA cross‐sectional cohort study using the publicly available National Health and Nutrition Examination Survey (NHANES 2017–2020) was conducted in individuals who completed the weight questionnaire and had accurate data for both weight at the time of survey and weight 10 years ago. To model the dynamics of obesity, a Markov transition state matrix was created, which allowed for the analysis of weight transitions over time. Bootstrap simulation was incorporated to account for uncertainty and generate multiple simulated datasets, providing a more robust estimation of the prevalence and trends in obesity within the cohort.ResultsOf the 6146 individuals who met the inclusion criteria, 3024 (49%) individuals were male and 3122 (51%) were female. There were 2252 (37%) White individuals, 1257 (20%) Hispanic individuals, 1636 (37%) Black individuals, and 739 (12%) Asian individuals. The average BMI was 30.16 (SD = 7.15), the average weight was 83.67 kilos (SD = 22.04), and the average weight change was a 3.27 kg (SD = 14.97) increase in body weight. A total of 2411 (39%) individuals lost weight, and 3735 (61%) individuals gained weight. 87 (1%) individuals were underweight (BMI <18.5), 2058 (33%) were normal weight (18.5 ≤ BMI <25), 1376 (22%) were overweight (25 ≤ BMI <30) and 2625 (43%) were in the obese category (BMI >30).ConclusionUnited States adults are at risk of transitioning from normal weight to the overweight or obese category. Markov modeling combined with bootstrap simulations can accurately model long‐term weight status.

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Section: Discussionmentioning

confidence: 99%