فهرست مطالب parastoo golpour

Analysis of mortality information helps in making better health policies. The present study discusses causes of mortality observed in Mashhad, Iran in 2017 using partitioning around medoids (PAM) and Clustering Large Applications (CLARA).

Data from 21838 subjects were collected from the “death registration system” of Mashhad University of Medical Sciences, Mashhad, Iran, and after data cleaning and dealing with incomplete data, individuals were clustered based on 4 variables: age, sex, leading cause of death and ​​place of residence using PAM and CLARA. The optimal number of clusters was determined before clustering using the mean of silhouette. Finally, the clustering performance of each algorithm was evaluated.

Based on the silhouette criterion, the highest difference among clusters was found when  five clusters were considered. In four of these clusters, which mostly included people aged over 65, circulatory, neoplasms, diseases of the respiratory system and endocrine, nutritional and metabolic diseases were the first to fourth leading causes of death, respectively. In the remaining cluster, the most common leading causes of death were congenital malformations, deformation and certain conditions originating in the perinatal period for children under 1 year of age.

Our results indicate that health policy makers should consider the importance of age distribution in clusters of a given population. Studying on death from cancer at the age of under 14 years and high percentage of death from diseases of the nervous system can contribute to health policies.

Coronary Artery Disease (CAD) is the most common type of cardiovascular disorders. Despite being costly and invasive, coronary angiography is a reliable method for diagnosing CAD. Therefore, it is crucial to use non-invasive methods to screen candidates for angiography to accelerate the process of decision-making. Two powerful Machine Learning (ML) methods are Random Forest (RF) and Artificial Neural Network (ANN).

The present study aimed to compare RF and ANN to define the most important features for positive CAD results and predict the need for angiography as a screening method.

This cross-sectional study was performed on 1128 patients referred for angiography. The data were divided into test and train sets. The models (RF and ANN) were fitted with the angiographic outcome variable (positive or negative) as the dependent variable and five features as predictors. Then, the performances of the models were compared by considering the Area Under the Rock Curve (AUC). All statistical analyses were done using the R software, version 4.1.2.

Out of the 1128 patients, 752 (66.7%) had positive angiography results. The AUC values were 0.75 and 0.52 for the test data set in ANN and RF models, respectively.

Fasting Blood Sugar (FBS), gender, age, Body Mass Index (BMI), and smoking habit were important in predicting the results of an angiography for CAD. Applying these factors in ML approaches can be considered a screen for angiography to accelerate the process of diagnosis.

Poisoning is a medical emergency, and is considered as a common cause of morbidity and mortality worldwide. In this study, the extended Cox model was used to determine the factors affecting the length of hospitalization in those with drug poisoning.

The sample size included 2408 patients with opioids poisoning referring to the Emergency Department of Imam Reza Hospital in Mashhad, Iran from March 21, 2018 to March 20, 2019. Extended Cox model was fitted to determine the effect of five covariates (age, gender, marital status, type of poisoning, and type of opioids). In survival analysis, the length of hospitalization was considered as a time covariate (T). Patients’ recovery was also regarded as an event.

Of 2408 patients, 399 (16.6%) were censored and 2009 (83.4%) were uncensored. The risk of failure in complete recovery from poisoning in males was 1.189 times more compared to females. The risk of failure in complete recovery for the 15-24, 25-44, 45-64, and >65 years age groups were 0.277, 0.241, 0.289, and 0.481 times lower, respectively compared to the <2 years age group. For the married patients, the risk was 0.291 times lower compared to the divorced patients. For those poisoned accidentally, the risk was 0.490 times lower than compared to those poisoned intentionally. For those used methadone, morphine, opium, and tramadol, the risk was 1.195, 1.243, 1.193, and 1.147 times more, respectively compared to those used marijuana. By increasing the time (day) of hospital stay, the risk of failure for the 25-44, 45-64, and >65 years age groups were 1.024, 1.028, and 1.040 times more, respectively compared to the <2 years age group. Moreover, for those poisoned accidentally, the risk was 1.197 times more compared to those poisoned intentionally by the time (day) of hospital stay.

The factors affecting the length of hospitalization in those poisoned by drugs are gender, marital status, and type of opioids covariate as time-independent covariate, and age and type of poisoning as time-dependent covariates. Since the complications of drug poisoning impose many costs on the health system, knowledge of these covariates can help take some measures for complete recovery of poisoned patients in a shorter length of hospital stay.

Pulmonary tuberculosis is one of the most common infectious diseases and second leading cause of death among infectious diseases. Sputum smear negative at the end of the second month of treatment is considered as the most significant method of recovery diagnosis. This research was conducted due to evaluating the effective factors of the sputum smear negative for pulmonary tuberculosis patients at the end of the second month of treatment. This cross-sectional study was conducted on 274 patients with smear positive pulmonary tuberculosis who were referred to health centers of Mashhad University of Medical Sciences from 2015 to 2016. Patients' demographics and clinical signs were extracted from their medical records and the effective factors of the sputum smear negativity at the end of the second month were evaluated by applying logistic regression At the end of the second month, 171 subjects (62.4 %) had smear negative, of whom 103 (60.2%) were women and 68 (64%) were men. In logistic regression, history of tuberculosis, the level of smear grade at the beginning of treatment and nationality are considered as significant factors. Smear grade at the beginning of treatment should be taken into account for pulmonary tuberculosis patients as the main marker of the response to treatment. However, by considering other factors, negative effects due to delay in treatment can be prevented.