Background Prediction of neonatal deaths in NICUs is important for benchmarking and evaluating healthcare services in NICUs. Application of machine learning techniques can improve physicians’ ability to predict the neonatal deaths. The aim of this study was to present a neonatal death risk prediction model using machine learning techniques. Methods This study was conducted in Tehran, Iran in two phases. Initially, important risk factors in neonatal death were identified and then several machine learning models including Artificial Neural Network (ANN), decision tree (Random Forest (RF), C5.0 and CHART tree), Support Vector Machine (SVM), Bayesian Network and Ensemble models were developed. Finally, we prospectively applied these models to predict neonatal death in a NICU and followed up the neonates to compare the outcomes of these neonates with real outcomes. Results 17 factors were considered important in neonatal mortality prediction. The highest Area Under the Curve (AUC) was achieved for the SVM and Ensemble models with 0.98. The best precision and specificity were 0.98 and 0.94, respectively for the RF model. The highest accuracy, sensitivity and F-score were achieved for the SVM model with 0.94, 0.95 and 0.96, respectively. The best performance of models in prospective evaluation was for the ANN, C5.0 and CHAID tree models. Conclusion Using the developed machine learning models can help physicians predict the neonatal deaths in NICUs.
Aim: To assess the effects of copper T-380-A intrauterine device (IUD) insertion on Candida species in cervicovaginal specimen by a molecular method, polymerase chain reaction. Methods: This is a longitudinal prospective study performed on 95 women attending Health Centers of Tehran, Iran in 2012, who selected copper T-380-A IUD for contraception and had no history of local or systemic antibiotics or antifungals use during the previous 2 weeks. Cervicovaginal specimens were twice collected and cultured on Sabouraud dextrose agar and CHROMagar Candida, before and 3 months after IUD insertion. Finally, a molecular method, PCR-RFLP was performed for identification of Candida species. P-values <0.05 were considered significant. Results: The mean age of participants was 28 AE 7.44 years. Positive Candida cultures were significantly increased 3 months after IUD insertion (25.3% vs 11.6%, P = 0.007). The most common identified species before and after IUD insertion, were Albicans, Glabrata and then both 'Albicans & Glabrata', respectively. The prevalence of Albicans and Glabrata decreased, while both 'Albicans & Glabrata' increased insignificantly. Conclusion: There was more than about fourfold increase in positive Candida cultures after IUD insertion. As the prevalence of simultaneous infection with both 'Albicans & Glabrata' species which are resistant to usual treatment, increased, it seems necessary to provide more intensive follow-up care for IUD users.
Background: Anticipating on in-birth Cardiopulmonary Resuscitation(CPR) in neonates is very important and complex. Timely identification and rapid CPR in neonates in the delivery room significantly affect reducing the mortality and other neurological disabilities. The aim of this study is to create a prediction system for identifying the need to in-birth CPR in neonates based on Machine Learning(ML) algorithms.Methods: In this study, 3882 neonatal medical records were retrospectively reviewed. Records were extracted from the maternal, fetal, and neonatal registry of Valiasr hospital in Tehran. A total of 60 risk factors were extracted, and five ML algorithms including J48, Naïve Bayesian, Multilayer Perceptron (MLP)، Support Vector Machine (SVM) and Random Forest were compared to predict the need to in-birth CPR in neonates. Also, using 10 feature selection algorithms, the features were ranked based on the importance, and using the ML algorithms, the important risk factors were identified. Results: In order to predict the need to in-birth CPR in neonates, SVM using all risk factors reached the accuracy of 88.43% and F-measure of 88.4%, while MLP using the 15 first important features reached the accuracy of 90.86% and the F-measure of 90.8%. The most important risk factors included gestational age, delivery type, presentation, steroid administration, macrosomia, prenatal care, infant number and rank, mother addiction, maternal chronic disease history, fetal hydrops, amniotic fluid, gestational hypertension, infertility and placental abruption. Conclusions: The proposed system can be useful in predicting the need to CPR in neonates in the delivery room.
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