Prediction of neonatal deaths in NICUs: development and validation of machine learning models
Abstract 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...
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doaj-e0f66ffe50084b5cbb4624169815bafa2021-04-25T11:43:39ZengBMCBMC Medical Informatics and Decision Making1472-69472021-04-0121111410.1186/s12911-021-01497-8Prediction of neonatal deaths in NICUs: development and validation of machine learning modelsAbbas Sheikhtaheri0Mohammad Reza Zarkesh1Raheleh Moradi2Farzaneh Kermani3Health Management and Economics Research Center, School of Health Management and Information Sciences, Iran University of Medical SciencesMaternal, Fetal and Neonatal Research Center, Tehran University of Medical SciencesFamily Health Institute, Maternal, Fetal and Neonatal Research Center, Tehran University of Medical SciencesHealth Information Technology Department, School of Allied Medical Sciences, Semnan University of Medical SciencesAbstract 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.https://doi.org/10.1186/s12911-021-01497-8Machine learningNeonateDeathPrediction |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Abbas Sheikhtaheri Mohammad Reza Zarkesh Raheleh Moradi Farzaneh Kermani |
spellingShingle |
Abbas Sheikhtaheri Mohammad Reza Zarkesh Raheleh Moradi Farzaneh Kermani Prediction of neonatal deaths in NICUs: development and validation of machine learning models BMC Medical Informatics and Decision Making Machine learning Neonate Death Prediction |
author_facet |
Abbas Sheikhtaheri Mohammad Reza Zarkesh Raheleh Moradi Farzaneh Kermani |
author_sort |
Abbas Sheikhtaheri |
title |
Prediction of neonatal deaths in NICUs: development and validation of machine learning models |
title_short |
Prediction of neonatal deaths in NICUs: development and validation of machine learning models |
title_full |
Prediction of neonatal deaths in NICUs: development and validation of machine learning models |
title_fullStr |
Prediction of neonatal deaths in NICUs: development and validation of machine learning models |
title_full_unstemmed |
Prediction of neonatal deaths in NICUs: development and validation of machine learning models |
title_sort |
prediction of neonatal deaths in nicus: development and validation of machine learning models |
publisher |
BMC |
series |
BMC Medical Informatics and Decision Making |
issn |
1472-6947 |
publishDate |
2021-04-01 |
description |
Abstract 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. |
topic |
Machine learning Neonate Death Prediction |
url |
https://doi.org/10.1186/s12911-021-01497-8 |
work_keys_str_mv |
AT abbassheikhtaheri predictionofneonataldeathsinnicusdevelopmentandvalidationofmachinelearningmodels AT mohammadrezazarkesh predictionofneonataldeathsinnicusdevelopmentandvalidationofmachinelearningmodels AT rahelehmoradi predictionofneonataldeathsinnicusdevelopmentandvalidationofmachinelearningmodels AT farzanehkermani predictionofneonataldeathsinnicusdevelopmentandvalidationofmachinelearningmodels |
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