Background: The scoring systems are helpful to monitor the PICU performance. One of the most important scoring systems is PRISM-III scoring system. Today PRISM score is used to evaluate the quality and quantity of the performance. The goal of this study was to show the prognosis of the patients admitted to Mofid pediatric intensive care unit (PICU) according to PRISM-III and PIM scoring systems.Materials and Methods: This study was a prospective descriptive one. Sampling method was sequentional and the sample size was 121 patients whom admitted to Mofid Hospital's PICU between Dec 2005 till May 2006. Results of PRISM-III scoring system were recorded in prepared sheets. Then, information transferred to computer for SPSS analysis. We used several statistical tests such as Chi square, t-test, Correlation and Regression, ANOVA, and Post-Hoc test.Results: Our study showed that 54% of patients were male and 46% were female and the most causes of admission to PICU were respiratory distress and central nervous system diseases, respectively. Sepsis was the main cause of death in PICU. The Mortality rate was 33%. The mean PRISM-III score was 7.57 and this score had 80% of sensitivity and 75% of specificity to predict the Mortality.Conclusion: According to ROC analysis the PRISM-III score has good predictive value in assessing the probability of Mortality in Mofid's PICU and increase in PRISM-III score corresponds to the higher incidence of Mortality.

Mortality forecasts are nowadays widely used to create and modify retirement pension schemes, disability insurance systems and other social security programmers. Experience shows that static life tables overestimate death probabilities. The reason for this overestimation is that static life tables, through being computed for a specific period of time, cannot take into account the decreasing Mortality trend over time. Dynamic life tables overcome this problem by incorporating the influence of the calendar when graduating Mortality.In this paper, we first apply the Lee-Carter model for estimation of Mortality rate. Then, we use parametric and semi parametric bootstrap Prediction intervals for Mortality trend. Finally, these methods are applied for analysis of Mortality data of Iran.

Purpose: Mortality is a dynamic process that completes over time and is a fundamental issue in life insurance, pension fund, health insurance, and in general any issue related to financial planning that deals with the longevity of individuals. Therefore, the accuracy of mathematical models in predicting Mortality rates is an important challenge. The purpose of this study is to generalize static stochastic Mortality models to dynamic stochastic Mortality models and to predict Mortality rates based on the generalization of stochastic Mortality models by the Cox-Ingersoll-Ross (CIR) process and to compare the results with each other.Methodology: In this research, two suggestions are presented: the first idea is to provide a dynamic correction method to increase the Prediction accuracy using the CIR process and the second idea is to examine the out-of-sample validation method.Findings: In this study, using the out-of-sample validation method, the force of Mortality from the best models selected from the two famous Mortality model families (Lee-Carter and Cairns, Blake and Dowd (CBD)) is compared with the results of the generalized model. After estimating the parameters of the studied models and calculating the Prediction of the Mortality rates, by calculating the mean absolute error and root mean squares error of Prediction, it is determined that the generalization of stochastic Mortality models by the CIR process performs much better than static Mortality models. The Bayesian information criterion also indicates that the use of generalized stochastic Mortality models is justified.Originality/Value: In this study, stochastic Mortality index models, which include Lee-Carter and Cairns-Blake-Dowd family models, are used and generalized by the CIR process. In this regard, Human Mortality Database (HMD) data is used. But there is no information about our country in this database. Because the French Mortality pattern is very close to the Iranian pattern and the life tables of this country (TD 88-90) are used in Iranian insurance applications, the crude death rate of French men in the years 1900-2018 on the ages of 18, 40 and 65 years is used. Using these data and the backtesting method, static Mortality models and generalized models with the CIR process are compared.

Background: Diagnosing patient deterioration and preventing unexpected deaths in the emergency department is a complex task that relies on the expertise and comprehensive understanding of emergency physicians concerning extensive clinical data. Objectives: Our study aimed to predict emergency department Mortality and compare different models. Methods: During a one-month period, demographic information and records were collected from 1, 000 patients admitted to the emergency department of a selected hospital in Tehran. We rigorously followed The Cross Industry Standard Process for data mining and methodically progressed through its sequential steps. We employed Cat Boost and Random Forest models for Prediction purposes. To prevent overfitting, Random Forest feature selection was employed. Expert judgment was utilized to eliminate features with an importance score below 0. 0095. To achieve a more thorough and dependable assessment, weimplemented a K-fold cross-validation method with a value of 5. Results: The Cat Boost model outperformed Random Forest significantly, showcasing an impressive mean accuracy of 0. 94 (standard deviation: 0. 03). Ejection fraction, urea (body waste materials), and diabetes had the greatest impact on Prediction. Conclusions: This study sheds light on the exceptional accuracy and efficiency of machine learning in predicting emergency department Mortality, surpassing the performance of traditional models. Implementing such models can result in significant improvements in early diagnosis and intervention. This, in turn, allows for optimal resource allocation in the emergency department, preventing the excessive consumption of resources and ultimately saving lives while enhancing patient outcomes.