Neonatal mortality prediction with routinely collected data: a machine learning approach

Abstract

Background Recent decreases in neonatal mortality have been slower than expected for most countries. This study aims to predict the risk of neonatal mortality using only data routinely available from birth records in the largest city of the Americas. Methods A probabilistic linkage of every birth record occurring in the municipality of São Paulo, Brazil, between 2012 e 2017 was performed with the death records from 2012 to 2018 (1,202,843 births and 447,687 deaths), and a total of 7282 neonatal deaths were identified (a neonatal mortality rate of 6.46 per 1000 live births). Births from 2012 and 2016 (N = 941,308; or 83.44% of the total) were used to train five different machine learning algorithms, while births occurring in 2017 (N = 186,854; or 16.56% of the total) were used to test their predictive performance on new unseen data. Results The best performance was obtained by the extreme gradient boosting trees (XGBoost) algorithm, with a very high AUC of 0.97 and F1-score of 0.55. The 5% births with the highest predicted risk of neonatal death included more than 90% of the actual neonatal deaths. On the other hand, there were no deaths among the 5% births with the lowest predicted risk. There were no significant differences in predictive performance for vulnerable subgroups. The use of a smaller number of variables (WHO’s five minimum perinatal indicators) decreased overall performance but the results still remained high (AUC of 0.91). With the addition of only three more variables, we achieved the same predictive performance (AUC of 0.97) as using all the 23 variables originally available from the Brazilian birth records. Conclusion Machine learning algorithms were able to identify with very high predictive performance the neonatal mortality risk of newborns using only routinely collected data.

Artificial intelligence and machine learning in pediatrics and neonatology healthcare

“…With an accuracy of 80%, a machine learning analysis of a 3-min speech can be used to detect children with anxiety or depression. 23…”

On usage of artificial intelligence for predicting mortality during and post-pregnancy: a systematic review of literature

“…When analyzing Figure 5, one can note that deep learning models appeared from 2019 on wards, showing that there may be a large field of search in relation to these models. Regarding the neonatal mortality works, ten machine learning models were proposed [29,21,10,22,25,31,26,27,28,24], two deep learning models were proposed [20,23], and seven logistic regression models were proposed [10,25,31,26,23,28,24]. Even though infant mortality was the focus of more works than stillbirth, the total of proposed models was the same, seven for each.…”

“…All seven works that used imbalanced data set used the AUC ROC metric; three used accuracy [21,14,24], which is one of the most sensible metric when working with imbalanced classes. The AUPRC metric, which according to Chicco et al [50], is the more robust metrics to evaluate a model performance when handling imbalancing, was only used by Batista et al [31].…”

“…Machine learning was the most common modeling technique found among the primary works of this SLR, being proposed by 16 of the 18 works [29,32,30,21,10,14,22,25,31,26,12,33,15,27,28,24]. Deep learning models in turn were proposed by four works [20,33,23,15].…”

“…Deep learning models in turn were proposed by four works [20,33,23,15]. In addition to machine learning and deep learning techniques, 11 works also presented Logistic Regression models [32,10,14,25,31,26,33,28,15,12,23].…”