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

Journal: BMC medical informatics and decision making
PMID: 36536413

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.

Authors

  • Elisson da Silva Rocha
    Programa de Pós-Graduação em Engenharia da Computação, Universidade de Pernambuco, Recife, Brazil.
  • Flavio Leandro de Morais Melo
    Programa de Pós-Graduação em Engenharia da Computação, Universidade de Pernambuco, Recife, Brazil.
  • Maria Eduarda Ferro de Mello
    Universidade Federal de Pernambuco, Recife, Brazil.
  • Barbara Figueiroa
    Programa Mãe Coruja Pernambucana, Secretaria de Saúde do Estado de Pernambuco, Recife, Brazil.
  • Vanderson Sampaio
    Instituto Todos pela Saúde, São Paulo, Brazil.
  • Patricia Takako Endo
    Programa de Pós-Graduação em Engenharia da Computação Universidade de Pernambuco (UPE) Recife Pernambuco Brazil.

Keywords

External Resources

Popular Topics
Recent Journals