Abstract
BackgroundPrediction 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.MethodsThis 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.Results17 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.ConclusionUsing the developed machine learning models can help physicians predict the neonatal deaths in NICUs.
Highlights
Prediction of neonatal deaths in Neonatal intensive care units (NICU) is important for benchmarking and evaluating healthcare services in neonatal intensive care units (NICUs)
Unlike the previous studies, which were performed on premature or Very Low Birth Weight (VLBW) neonates [3, 13], or in general settings other than NICUs [5, 6, 12], the present study considers all neonates without birth weight limitation in NICU settings
After implementing several machine learning algorithms on these five sets of features, we found that the better results were obtained for models developed based on 17 and 12 features, most models developed based on 17 features had the highest performance
Summary
Prediction of neonatal deaths in NICUs is important for benchmarking and evaluating healthcare services in NICUs. The neonatal period is the first 28 days of life, which is the stage of developing physiological adaptations for extra-uterine life. This time is a vulnerable period and the high neonatal mortality rate is due to the high level of vulnerability in this period [1]. In order to public health policy-making and management of pregnancy, childbirth and neonate periods, including the proper selection of risk factors and Sheikhtaheri et al BMC Med Inform Decis Mak (2021) 21:131 development of selective care pathways for high-risk pregnancies, it is important to predict high-risk neonates [6]. In NICUs, decision-making is a complex and important process, and the use of artificial intelligence and machine learning techniques can improve the quality of neonatal care by providing early warnings to healthcare providers [11]
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