Volume 14, Issue 1 (Vol 14, No 1, 2018)
irje 2018, 14(1): 74-82 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Ghorbani Gholiabad S, Sadeghifar M, Ghorbani Gholiabad R, Hamidi O. Forecasting Frequency of Delivery using Poisson Hidden Markov Models . irje 2018; 14 (1) :74-82
URL: http://irje.tums.ac.ir/article-1-5966-en.html
URL: http://irje.tums.ac.ir/article-1-5966-en.html
1- PhD Student of Biostatistics, Department of Biostatistics & Epidemiology, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran
2- Associate Professor of Biostatistics, Department of Statistics, Faculty of Basic Sciences, Bu-Ali Sina University, Hamadan, Iran ,sadeghifar@basu.ac.ir
3- MS of Statistics, Hakim Jorjani Hospital, Gorgan, Iran
4- Coach, Department of Science, Hamadan University of Technology, Hamadan, Iran
2- Associate Professor of Biostatistics, Department of Statistics, Faculty of Basic Sciences, Bu-Ali Sina University, Hamadan, Iran ,
3- MS of Statistics, Hakim Jorjani Hospital, Gorgan, Iran
4- Coach, Department of Science, Hamadan University of Technology, Hamadan, Iran
Abstract: (5006 Views)
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.
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.
Type of Study: Research |
Subject:
General
Received: 2018/06/25 | Accepted: 2018/06/25 | Published: 2018/06/25
Received: 2018/06/25 | Accepted: 2018/06/25 | Published: 2018/06/25
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
