Estimation of Neonatal Intestinal Perforation Associated with Necrotizing Enterocolitis by Machine Learning Reveals New Key Factors.
Int J Environ Res Public Health
; 15(11)2018 11 09.
Article
en En
| MEDLINE
| ID: mdl-30423965
ABSTRACT
Intestinal perforation (IP) associated with necrotizing enterocolitis (NEC) is one of the leading causes of mortality in premature neonates; with major nutritional and neurodevelopmental sequelae. Since predicting which neonates will develop perforation is still challenging; clinicians might benefit considerably with an early diagnosis tool and the identification of critical factors. The aim of this study was to forecast IP related to NEC and to investigate the predictive quality of variables; based on a machine learning-based technique. The Back-propagation neural network was used to train and test the models with a dataset constructed from medical records of the NICU; with birth and hospitalization maternal and neonatal clinical; feeding and laboratory parameters; as input variables. The outcome of the models was diagnosis (1) IP associated with NEC; (2) NEC or (3) control (neither IP nor NEC). Models accurately estimated IP with good performances; the regression coefficients between the experimental and predicted data were R² > 0.97. Critical variables for IP prediction were identified neonatal platelets and neutrophils; orotracheal intubation; birth weight; sex; arterial blood gas parameters (pCO2 and HCO3); gestational age; use of fortifier; patent ductus arteriosus; maternal age and maternal morbidity. These models may allow quality improvement in medical practice.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Redes Neurales de la Computación
/
Enterocolitis Necrotizante
/
Aprendizaje Automático
/
Perforación Intestinal
Tipo de estudio:
Etiology_studies
/
Prognostic_studies
/
Risk_factors_studies
/
Screening_studies
Límite:
Adolescent
/
Adult
/
Female
/
Humans
/
Male
/
Newborn
Idioma:
En
Revista:
Int J Environ Res Public Health
Año:
2018
Tipo del documento:
Article
País de afiliación:
México