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

Abstract

Background: Care during pregnancy, childbirth and puerperium are fundamental to avoid pathologies for the mother and her baby. However, health issues can occur during this period, causing misfortunes, such as the death of the fetus or neonate. Predictive models of fetal and infant deaths are important technological tools that can help to reduce mortality indexes. The main goal of this work is to present a systematic review of literature focused on computational models to predict mortality, covering stillbirth, perinatal, neonatal, and infant deaths, highlighting their methodology and the description of the proposed computational models.

Methods: We conducted a systematic review of literature, limiting the search to the last 10 years of publications considering the five main scientific databases as source.

Results: From 671 works, 18 of them were selected as primary studies for further analysis. We found that most of works are focused on prediction of neonatal deaths, using machine learning models (more specifically Random Forest). The top five most common features used to train models are birth weight, gestational age, sex of the child, Apgar score and mother's age. Having predictive models for preventing mortality during and post-pregnancy not only improve the mother's quality of life, as well as it can be a powerful and low-cost tool to decrease mortality ratios.

Conclusion: Based on the results of this SRL, we can state that scientific efforts have been done in this area, but there are many open research opportunities to be developed by the community.

Keywords: Deep learning; Infant mortality; Machine learning; Neonatal mortality; Stillbirth.

Fig. 1

PRISMA flowchart of the review process

Fig. 2

Number of selected works by type of mortality

Fig. 3

Definition of deaths that occur during or post-pregnancy up to 1 year from birth

Fig. 4

Common features used by primary works, considering fetal, neonatal and infant death

Fig. 5

Type of modeling technique by the year of work publication

Fig. 6

Number of primary works and the modeling technique that was proposed based on the type of mortality classification

Fig. 7

Machine learning techniques and number of proposed models found in primary works

Fig. 8

Deep learning techniques and number of proposed models found in primary works

Fig. 9

The most common metrics used to evaluate model performance when working with imbalanced data set