A theory to the stochastic dynamic model building for chronic progressive disease processes with an application to chronic gastritis

1976 ◽  
Vol 57 (1) ◽  
pp. 131-152 ◽  
Author(s):  
E. Hovinen ◽  
M. Kekki ◽  
S. Kuikka
2020 ◽  
Vol 98 (3) ◽  
pp. 178-184
Author(s):  
T. V. Chernyakova ◽  
A. Yu. Brezhnev ◽  
I. R. Gazizova ◽  
A. V. Kuroyedov ◽  
A. V. Seleznev

In the review we have integrated all up-to-date knowledge concerning clinical course and treatment of glaucoma among pregnant women to help specialists choose a proper policy of treatment for such a complicated group of patients. Glaucoma is a chronic progressive disease. It rarely occurs among childbearing aged women. Nevertheless the probability to manage pregnant patients having glaucoma has been recently increasing. The situation is complicated by the fact that there are no recommendations on how to treat glaucoma among pregnant women. As we know, eye pressure is progressively going down from the first to the third trimester, so we often have to correct hypotensive therapy. Besides, it is necessary to take into account the effect of applied medicines on mother health and evaluate possible teratogenic complications for a fetus. The only medicine against glaucoma which belongs to category B according to FDA classification is brimonidine. Medicines of the other groups should be prescribed with care. Laser treatment or surgery may also be a relevant decision when monitoring patients who are planning pregnancy or just bearing a child. Such treatment should be also accompanied by medicines.


2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Rahi Jain ◽  
Wei Xu

Abstract Background Developing statistical and machine learning methods on studies with missing information is a ubiquitous challenge in real-world biological research. The strategy in literature relies on either removing the samples with missing values like complete case analysis (CCA) or imputing the information in the samples with missing values like predictive mean matching (PMM) such as MICE. Some limitations of these strategies are information loss and closeness of the imputed values with the missing values. Further, in scenarios with piecemeal medical data, these strategies have to wait to complete the data collection process to provide a complete dataset for statistical models. Method and results This study proposes a dynamic model updating (DMU) approach, a different strategy to develop statistical models with missing data. DMU uses only the information available in the dataset to prepare the statistical models. DMU segments the original dataset into small complete datasets. The study uses hierarchical clustering to segment the original dataset into small complete datasets followed by Bayesian regression on each of the small complete datasets. Predictor estimates are updated using the posterior estimates from each dataset. The performance of DMU is evaluated by using both simulated data and real studies and show better results or at par with other approaches like CCA and PMM. Conclusion DMU approach provides an alternative to the existing approaches of information elimination and imputation in processing the datasets with missing values. While the study applied the approach for continuous cross-sectional data, the approach can be applied to longitudinal, categorical and time-to-event biological data.


Author(s):  
Lijuan Li ◽  
Yongdong Chen ◽  
Bin Zhou ◽  
Hongliang Liu ◽  
Yongfei Liu

AbstractWith the increase in the proportion of multiple renewable energy sources, power electronics equipment and new loads, power systems are gradually evolving towards the integration of multi-energy, multi-network and multi-subject affected by more stochastic excitation with greater intensity. There is a problem of establishing an effective stochastic dynamic model and algorithm under different stochastic excitation intensities. A Milstein-Euler predictor-corrector method for a nonlinear and linearized stochastic dynamic model of a power system is constructed to numerically discretize the models. The optimal threshold model of stochastic excitation intensity for linearizing the nonlinear stochastic dynamic model is proposed to obtain the corresponding linearization threshold condition. The simulation results of one-machine infinite-bus (OMIB) systems show the correctness and rationality of the predictor-corrector method and the linearization threshold condition for the power system stochastic dynamic model. This study provides a reference for stochastic modelling and efficient simulation of power systems with multiple stochastic excitations and has important application value for stability judgment and security evaluation.


2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Ghazwan Butrous

[No abstract. Showing first paragraph of article]Pulmonary hypertension is a progressive disease characterized by an elevation of pulmonary artery pressure and pulmonary vascular resistance, leading to right ventricular failure and death. It remains a challenging chronic progressive disease, but the current interest and advent of medical therapy in the last 20 years has significantly changed the perception of medical community in this disease. Pulmonary hypertension is not a specific disease; the majority of cases present with other diseases and various pathological processes that affect the pulmonary vasculature, and consequently increase pulmonary pressure and vascular resistance.


Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-18 ◽  
Author(s):  
Tao Li ◽  
Bin Zhang

The corrosion reactions in concrete materials subjected to external environment attack can lead to the deterioration of concrete. However, the effects of internal fluctuations on the corrosion reaction process have not been reported in current studies on damage of concrete materials. To comprehensively describe the effects of internal fluctuations, the stochastic dynamic model of corrosion reactions in concrete materials subjected to sulfate attack is established based on the law of mass conservation and random process theory, in which internal fluctuations and the parameters of the chemical system are, respectively, regarded as colored Gaussian noises and a series of random variables. An experiment of sulfate corrosion reactions in concrete material is carried out to verify the effectiveness of the proposed method. Furthermore, the effects of variations of the initial reactant concentrations on the concentration evolution processes of the corrosion products are investigated. Results show that the stochastic dynamical responses of the corrosion reactions in concrete can be comprehensively investigated by the proposed stochastic mathematical model; the probabilistic information of the corrosion products can also be obtained conveniently. The concentration evolution process of sulfate corrosion products is a random process. The experimental data are only some samples of the random process. Concentrations of the corrosion products in concrete materials significantly fluctuate with the variations of the initial reactant concentrations.


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