Evaluation of artificial intelligence models and classical statistics models of time Series in forecasting the number of hospital inpatient admissions

Introduction: The study and analysis of each health system has become a necessity for its performance improvement through time. In this context, management and analysis of the number of patients is an important factor in the process of improving managers' decisions. The aims of this study were to explore and evaluate the use of multiple time series forecasting methods to predict monthly hospital inpatient admissions at six public hospitals in Mashhad city and to compare the accuracy performance of these methods

Methods: This cross-sectional modeling study was performed based on monthly data of inpatient admissions at six public hospitals in Mashhad from March 2004 through March 2016. Data were extracted from database of the Statistics Office of Mashhad University of Medical Sciences. Holt-winters, Seasonal Autoregressive Integrated Moving Average [SARIMA], Multilayer Perceptron [MLP] and Generalized Regression Neural Networks [GRNN] models were applied to forecast monthly inpatient numbers at each hospital. The error of the models in regard to the predicted values was reported through Mean Absolute Percentage Error [MAPE]

Results: Holt-Winters method, due to providing the optimal forecasting performance in four hospitals, could be an efficient method for predicting the number of inpatients in hospitals. Totally, the studied models with a MAPE from 2.13 percent to 4.12 percent showed acceptable performance in all six hospitals

Conclusion: Time series analysis is an adequate practical tool for predicting the number of hospital inpatient admissions. Given the unique characteristics of different hospitals, applied methods in this study, including modeling and data analysis can be used in other hospitals to improve their resource allocation and strategic planning

Evaluation of the role of TAX, HBZ, and HTLV-1 proviral load on the survival of ATLL patients

BACKGROUND: Adult T-cell leukemia/lymphoma (ATLL) is an aggressive malignancy with very poor prognosis and short survival, caused by the human T-lymphotropic virus type-1 (HTLV-1). The HTLV-1 biomarkers trans-activator x (TAX) and HTLV-1 basic leucine zipper factor (HBZ) are main oncogenes and life-threatening elements. This study aimed to assess the role of the TAX and HBZ genes and HTLV-1 proviral load (PVL) in the survival of patients with ATLL. METHODS: Forty-three HTLV-1-infected individuals, including 18 asymptomatic carriers (AC) and 25 ATLL patients (ATLL), were evaluated between 2011 and 2015. The mRNA expression of TAX and HBZ and the HTLV-1 PVL were measured by quantitative PCR. RESULTS: Significant differences in the mean expression levels of TAX and HBZ were observed between the two study groups (ATLL and AC, P=0.014 and P=0.000, respectively). In addition, the ATLL group showed a significantly higher PVL than AC (P=0.000). There was a significant negative relationship between PVL and survival among all study groups (P=0.047). CONCLUSION: The HTLV-1 PVL and expression of TAX and HBZ were higher in the ATLL group than in the AC group. Moreover, a higher PVL was associated with shorter survival time among all ATLL subjects. Therefore, measurement of PVL, TAX, and HBZ may be beneficial for monitoring and predicting HTLV-1-infection outcomes, and PVL may be useful for prognosis assessment of ATLL patients. This research demonstrates the possible correlation between these virological markers and survival in ATLL patients.

Determination of the most important risk factors of gestational diabetes in Iran by group analytical hierarchy process

Background: The outbreak of gestational diabetes has a significant increase during recent years. This disease has complications for mother and her baby. Screening is an opportunity for preventing of gestational diabetes complications

Objective: The aim of this research was to determine the most important risk factors for Gestational Diabetes Mellitus [GDM] in Iran according to the expert's views by Group Analytical Hierarchy Process

Materials and Methods: In this cross-sectional study, papers related to the prevalence and risk factors of GDM in Iran from 1992-2015 were reviewed. By studying texts and Up to Date databases, 10 risk factors for gestational diabetes were collected. Among these 10 items, the risk factors that have become significant based on studying literature in Iran were selected for analysis. Group Analytical Hierarchy Process [GAHP] questionnaire distributed among all experts

Results: 8 risk factors of gestational diabetes were significant in Iran. The analysis of experts' views showed that "History of GDM or disorder in glucose tolerance in pregnancy" is the most important risk factor for developing GDM [40.7%]. The second and third most important risk factors were "History of macrosomia [infant birth weight > 4.1 Kg]" [20.2%] and" History of diabetes in first degree relatives" [10.7%]

Conclusion: Suggesting screening based on the determined order of these risk factors can reduce the cost and stress in pregnant women. Also, it makes patient identifying faster. The healthcare sector can consider these priorities determined in experts' views to prevent gestational diabetes