S Ghorbani Gholiabad , M Sadeghifar, R Ghorbani Gholiabad , O Hamidi,
Volume 14, Issue 1 (6-2018)
Abstract
Background and Objectives: Delivery is one of the most important services in the health systems, and increasing its effectiveness and efficiency are a health priorities. The aim of this study was to forecast the number of deliveries in order to design plans for using all facilities to provide patients with better services.
Methods: The data used in this study were the number of deliveries per month in Hakim Jorjani Hospital, Gorgan, Iran during the years 2010 to 2016. Due to the over-dispersion of the data and non-compliance with a Poisson distribution, the Poisson hidden Markov model was applied to predict the frequency of monthly deliveries. The model parameters were estimated using the maximum likelihood method and expectation maximization algorithm.
Results: The use of the Akaike criteria revealed the frequency of delivery in different months in the hospital followed a Poisson hidden Markov models with three hidden states, and the mean Poisson distribution in each component was 193.74, 236.05, and 272.61 labors, respectively.
Conclusion: The results of this study showed that government’s encouraging policies have had short-term, limited effects on increasing fertility with minimal effects on the results of the two-year forecast.
M Safari, M Sadeghifar, Gh Roshanaei , A Zahiri,
Volume 14, Issue 2 (9-2018)
Abstract
Background and Objectives: Tuberculosis is a chronic bacterial disease and a major cause of morbidity and mortality. It is caused by a Mycobacterium tuberculosis. Awareness of the incidence and number of new cases of the disease is valuable information for revising the implemented programs and development indicators. time series and regression are commonly used models for prediction but these methods require some assumptions. The purpose of this study was to predict new TB cases using the hidden Markov model which does not require many assumption.
Methods: The data used in this study was the monthly number of new TB cases during 2006-2016 identified and recorded in Hamedan Province. Rorecasting the number of new TB cases was done using hidden Markov models using the hidden Markov package in the R software.
Results: According to the AIC and BIC criterion, two states had the best fit to the data, i.e. the data of this study were a mixture of two Poisson distributions with average number of event 5.96 and 10.2 respectively. The results also predicted the number of new cases over the next 24 months based on the hidden Markov model would be between 8 and 9 new cases in each month.
Conclusion: The hidden Markov model is the best model for prediction using the Markov chain. This model, in addition to detection of an appropriate model for the available data, can determine the transition probability matrix, which can help physicians predict the future state of the disease and take preventive measures befor reaching advanced stages.
