Showing 5 results for Zahiri
S Khazaei, Z Kousehlou, M Karami, A Zahiri , J Bathaei,
Volume 9, Issue 1 (5-2013)
Abstract
Background & Objectives: Tuberculosis (TB) is the largest single cause of death from infectious diseases and has a ten rating of global burden of disease. Despite the availability of effective treatment for pulmonary TB, sputum conversion of patients affected by various factors. This study aimed to determine the time course of sputum conversion in patients and possible affected factors in this process.
Methods: In this Retrospective cohort study, 440 patients with smear-positive pulmonary TB in Hamadan province from 21 March 2006 to 20 March 2012 referred to health centers were included. Demographic, clinical data and treatment status of patients, including the time of sputum smear negative were extracted using TB Register software from patient registry. Time to sputum conversion was considered monthly during the patient’s treatmentand shown by Kaplan-Meier survival curve. The effects of some determinants including gender, location, age group and number of bacilli in the sputum of patients at the beginning of treatment were determined using Cox proportional hazard regression model.
Results: From 440 patients with smear-positive pulmonary TB, 51% (221 patients) were male and 49% (219 patients) were female. Fifty seven percent (57%) were living in urban and others in rural. The median of sputum conversion was 3 months. Totally, sputum conversion rate at the end of month 2 and 3 were 69% and 88%, respectively.
Conclusion: Our findings revealed that there has been a considerable difference between the expected sputum conversion rate and the observed rate in Hamadan province, Iran.
S Setareh, M Zahiri Esfahani , M Zare Bandamiri , A Raeesi, R Abbasi,
Volume 14, Issue 1 (Vol 14, No 1, 2018)
Abstract
Background and Objectives: Colon cancer is the third most common cancer in the world and the fourth most common cancer in Iran. It is very important to predict the cancer outcome and its basic clinical data. Due to to the high rate of colon cancer and the benefits of data mining to predict survival, the aim of this study was to survey two widely used machine learning algorithms, Bagging and Support Vector Machines (SVM), to predict the outcome of colon cancer patients.
Methods: The population of this study was 567 patients with stage 1-4 of colon cancer in Namazi Radiotherapy Center, Shiraz in 2006-2011. Three hundred and thirty eight patients were alive and 229 patients were dead. We used the Support Vector Machines (SVM) and Bagging methods in order to predict the survival of patients with colon cancer. The Weka software ver 3.6.10 was used for data analysis.
Results: The performance of two algorithms was determined using the confusion matrix. The accuracy, specificity, and sensitivity of the SVM was 84.48%, 81%, and 87%, and the accuracy, specificity, and sensitivity of Bagging was 83.95%, 78%, and 88%, respectively.
Conclusion: The results showed both algorithms have a high performance in survival prediction of patients with colon cancer but the Support Vector Machines has a higher accuracy.
M Safari, M Sadeghifar, Gh Roshanaei , A Zahiri,
Volume 14, Issue 2 (Vol.14, No.2, 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.
M Karami, S Khazaei, F Shahbazi, M Mirzaei, A Biglarkhani, A Ataei, Seyed Jalalodin Bathaei, A Zahiri, M Shojaeyan, R Zamani, Ae Karshenas, F Heeders-Moghis, K Hamelmann, R Heidari Moghadam, I Khodadadi-Kahlan, S Bashirian, F Keramat, Sh Hashemi, E Jalili, F Azizi-Jalilian,
Volume 17, Issue 3 (Vol 17,No.3, Atumn 2021 2021)
Abstract
Background and Objectives: The aim of this study was to investigate the epidemiological characteristics of patients with Covid-19 in Hamadan Province.
Materials and Methods: In this descriptive cross-sectional study, demographic and epidemiological data of all people who presented to hospitals in Hamadan Province from February 2019 to December 2020 were extracted using two checklists. Data were then analyzed using the Stata software.
Results: In this study, 9674 covid-19 positive patients were examined. According to results, 49.11% of the cases occurred in the elderly over 60 years. The rate of involvement was higher in females than males (51.57% vs. 48.43%). Moreover, 72.05% of the definitive patients lived in the city and 0.76% reported traveling to areas with a high prevalence of the disease two weeks before the onset of symptoms. The highest incidence of this disease per one hundred thousand population was in Malayer, Hamedan and Nahavand counties and the highest fetality was in Razan and Dargazin, Tuyserkan and Asadabad counties, respectively. The data of suspected, probable, and definite outpatients with Covid-19 presenting to medical centers were not evaluated in this study.
Conclusion: Due to the high death rate in the elderly, males, those with underlying diseases, and people living in rural areas, it is necessary to design and apply precautionary measures in these groups. Attention should be paid to these high-risk groups in the shortest possible time to reduce the burden of this disease on individuals as well as the health care system.
Fatemeh Shahbazi, Salman Khazaei, Mohammad Mirzaei, Seyed Jalalodin Bathaei, Ali Zahiri, Manoochehr Karami,
Volume 18, Issue 4 (Vol.18, No.4, Winter 2023)
Abstract
Background and Objectives: The purpose of this study was to ascertain the mortality rate and years of life lost (YLL) resulting from COVID-19 infection in Hamadan Province.
Methods: In this cross-sectional study, information regarding the number of deaths caused by COVID-19 infection was obtained from the Vice-Chancellor of Health at Hamadan University of Medical Sciences. The research period spanned from February 2020 to February 2021. The deaths recorded by the deputy health department encompassed both outpatients and inpatients. The calculation of Years of Life Lost (YLL) was based on the guidelines outlined in the Global Burden of Disease (GBD) 2010. All data analysis was performed using Excel software.
Results: During the study period, a total of 1556 deaths occurred due to COVID-19 infection in Hamadan Province. The years of life lost (YLL) due to premature death were 15783 years (49.99 per thousand) in men, 12794 years (38.43 per thousand) in women, and 28577 years (44.08 per thousand) in both sexes. Specifically, urban areas accounted for 19824 YLL, while rural areas accounted for 8753 YLL.
Conclusion: Based on the findings of this study, COVID-19 infection resulted in a significant loss of potential years of life, particularly among men, those over 60 years of age, and in urban areas. As a result, intervention programs should prioritize the importance of early detection of the disease, reducing its severity, and subsequently mitigating the number of fatalities. Additionally, better control of the disease in elderly populations, who represent the highest proportion of years of life lost, should be a focal point.
