title: Machine learning for development of an expert system to predict premature birth.
abstract: Normal pregnancy involves a term of 40 weeks gestation. Problems associated with low birthweight and prematurity continue to plague childbearing families and the healthcare system because 8-12% of all newborns in the United States deliver prior to 37 weeks gestation. The high cost of caring for premature babies increasingly treats all pregnant women as if they are 'high risk' for preterm birth. Artificial intelligence techniques used a machine learning program named LERS1 with large datasets (n = 18,890; 214 variables), statistical analysis, expert verification techniques, and a prototype expert system2 that yielded improved accuracy (53-90%) over existing manual techniques (17-38%) for predicting preterm birth.
authors.affiliation: School of Nursing, University of Missouri, Columbia 65211, USA.
authors.forename: L K
authors.initials: LK
authors.lastname: Woolery
authors.forename: J
authors.initials: J
authors.lastname: Grzymala-Busse
mesh_terms: D000328:Adult; D001185:Artificial Intelligence; D016208:Databases, Factual; D003662:Decision Theory; D005103:Expert Systems; D005260:Female; D006801:Humans; D007231:Infant, Newborn; D007752:Obstetric Labor, Premature / Q000209:etiology* / Q000517:prevention & control*; D011247:Pregnancy