Introduction : Using data mining to develop prediction models from large amounts of data in medicine has become increasingly popular during the recent years. In this study, we intend to use a decision tree data mining algorithm in order to identify factors influencing mortality in burn patients.

  Methods: The present retrospective descriptive study is based on burning patients’ records. Overall, the medical records of 4804 patients were scrutinized. The collected data were analyzed using statistical software (SPSS version 16), data mining software (Clementine version 12), and CHAID algorithm.

  Results: The resulting model for predicting survival and mortality of burning patients included the percentage of Total Burn Surface Area (TBSA %), degree of burn, length of stay, gender, and age of patients. Other variables including blood cultures, wound cultures, urine cultures, and the months of patient hospitalization had no effect on improving the efficiency of the model.

  Conclusion: The proposed model is valid and reliable due to its accuracy (approximately 95%). In fact, the results of this study, some of which are consistent with the results of other studies, can propose a comprehensive, accurate, and reliable local model for predicting mortality and survival of burning patients based on the mentioned variables. Thus, this local model can be used as an important criterion for evaluating the effective treatment of burn patients.

Introduction: In recent years, one of the concepts that has attracted industrial and organizational psychologists’ attention is employees’ exhaustion called burnout having a direct relationship with inefficiency of human capital.
Methods: This is an applied descriptive survery. The study population was 230 employees of East Azerbaijan province welfare. The data were collected by a researcher- made questionnaire based on on the theory of Bardvik and the burnout theory of Maslach. For assessing the validity (Symbolic or formal) and thee reliability SPSS was used which showed 0/760 and 0/880, respectively. The data were analyzed by descriptive and inferential statistics.
Result: All variables in the research hypotheses, other than those of the first and second hypotheses, appeared to have a significant correlation, with a correlation coefficient of 0. 056 which is less than less than 0.05. According to the statistics tables and linear regression analysis, it could be concluded that per unit change in the independent variable is explained by a change in the dependent variable.
 Conclusion: The results indicate that, career plateau and its dimensions, except for the lack of learning, are effective on staff burnout in Welfare Organization of East Azerbaijan province.