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

Author(s)

Batista, Andre F. M. ^[1] ; Diniz, Carmen S. G. ^[2] ; Bonilha, Eliana A. ^[3] ; Kawachi, Ichiro ^[4] ; Chiavegatto Filho, Alexandre D. P. ^[1]

Affiliation

^[1] Univ Sao Paulo, Sch Publ Hlth, Dept Epidemiol, 715 Av Dr Arnaldo, BR-01246904 Sao Paulo, SP - Brazil

^[2] Univ Sao Paulo, Sch Publ Hlth, Dept Hlth Life Cycles & Soc, Sao Paulo - Brazil

^[3] Municipal Dept Hlth Sao Paulo, Sao Paulo - Brazil

^[4] Harvard Univ, Harvard TH Chan Sch Publ Hlth, Dept Social & Behav Sci, Cambridge, MA 02138 - USA

• Document type: Journal article
• Source: BMC PEDIATRICS; v. 21, n. 1 JUL 21 2021.
• Web of Science Citations: 0

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 Sao 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. (AU)

• FAPESP's process: 17/09369-8 - Cause-specific mortality prediction with machine learning on a longitudinal sample of 502,632 individuals
• Grantee: Alexandre Dias Porto Chiavegatto Filho
• Support Opportunities: Regular Research Grants