A Predictive Analytic Model for Maternal Morbidity

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This chapter presents a predictive analytic model for preventing neonatal morbidity through the analysis of patterns of risky behavior regarding morbidity in newborns. The chapter presents the design and implementation of a forecasting model of Neonatal morbidity. The model developed is based on art...

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Autores principales: Edgardo Palza V, Jorge Sanchez, Guillermo Mamani, Percy Pacora, Alain Abran, Jane Dominique Moon
Formato: Capítulo de libro
Lenguaje:inglés
Publicado: 2019
Materias:
neonatal morbidity
maternal morbidity
predictive analytics
Bayesian networks
influence diagrams
risk factors
newborn health
medical records
data mining
forecasting models
Artificial Intelligence in Healthcare
Maternal and Neonatal Healthcare
Data Mining and Machine Learning Applications
Acceso en línea:Enlace permanente (DOI o Handle)
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Sumario:This chapter presents a predictive analytic model for preventing neonatal morbidity through the analysis of patterns of risky behavior regarding morbidity in newborns. The chapter presents the design and implementation of a forecasting model of Neonatal morbidity. The model developed is based on artificial intelligence using Bayesian Networks, Influence Diagrams and principles of traditional statistics. The model research is based on a repository of 10,000 medical records at a hospital in Peru. The model aims to identify the factors that are causes of morbidity in newborns, is based on data mining techniques and developed using the CRISP-DM methodology.

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