Journal of

Background and Aim: Neonatal jaundice is a matter that is very important for clinicians all over the world because this disease is one of the most common cases that requires clinical care. The aim of this study is to use data classification algorithms to predict the type of jaundice in neonates, and therefore, to prevent irreparable damages in future.
Materials and Methods: This is a descriptive study and is done with the use of neonatal jaundice dataset that has been collected in Cairo, Egypt. In this study, after preprocessing the data, classification algorithms such as decision tree, Naïve Bayes, and kNN (k-Nearest Neighbors) were used, compared and analyzed in Orange application.
Results: Based on the findings, decision tree with precision of 94%, Naïve Bayes with precision of 91%, and kNN with precision of 89% can classify the types of neonatal jaundice. So, among these types, the most precise classification algorithm is decision tree. 
Conclusion: Classification algorithms can be used in clinical decision support systems to help physicians make decisions about the types of special diseases; therefore, physicians can look after patients appropriately. So the probable risks for patients can be decreased. 

Background and Aim: Antibiotics are one of the most commonly used drugs that help to heal and treat diseases, but improper administration and excessive use of them can increase length of stay and cost of hospitalization. The purpose of this study was to analyze relationship between experimental use of antibiotics and duration of stay of premature infants admitted to Mahdiyeh hospital in Tehran.
Materials and Methods: This descriptive-analytical and cross-sectional study was carried out in 2019 on 159 premature infants admitted to intensive care ward of Mahdiyeh hospital in Tehran were investigated. The study tool was a questionnaire that validity of questionnaire was measured by content analysis method and reliability of questionnaire was measured by Cronbach’s alpha method. Data analysis was done using SPSS software, with help of descriptive statistics of frequency and percentage and statistical test of one-way analysis of variance and independent t-test.
Results: The results showed that 52.2% of infants were girls and 79.9% of deliveries were performed by cesarean section. The average length of stay of infants was 31.47 days and average birth of infants was 30 weeks. According to results of study, there was a significant relationship between length of hospitalization of infants with duration of antibiotic treatment and number of periods of receiving antibiotics (P<0.05). The duration of hospitalization of infants with a period of receiving less than 14 days of antibiotics was almost half of duration of hospitalization of infants with more than 14 days of receiving antibiotics. Also, there was a significant relationship between number of antibiotic courses and length of hospitalization of patients (P<0.05). With increase in number of antibiotic sessions, duration of hospitalization of infants increased significantly. The results of study showed that there was a significant relationship between birth weight of babies, cost of bed, and total cost of experimental administration of antibiotics (P<0.05).
Conclusion: Considering that rational use of antibiotics reduce length of stay of hospitalized infants, Ministry of Health should develop an updated and transparent antibiotic adminstration protocol for premature infants and awareness physicians to inform particularly prescription of antibiotics and implementation of stewardship antibiotic programs in neonatal intensive care unit. Also the publication of standard treatment guidelines, instructions including importance of prescribing antibiotics with correct dose and clinical education of physicians are necessary.