title: MENTOR: a Bayesian Model for prediction of mental retardation in newborns.
abstract: Mental retardation (MR) is a diagnosis that is made with extreme caution because of the many uncertainties in its etiology and prognosis. In fact, most physicians will delay the diagnosis for months or years so that substantial evidence is available to rule the diagnosis in or out. MENTOR is a Bayesian Model for the prediction of MR in newborns that provides probabilities for the full range of cognitive outcomes, ranging from MR to superior intelligence. Using the model to confirm clinical judgment could help physicians decide when to proceed with diagnostic tests. The physician and family could discuss the probabilities for MR, borderline, normal, and superior intelligence, given the child's status in infancy and base their decision about additional testing, in part, on this information.
authors.affiliation: Department of Information and Computer Science, University of California at Irvine 92697, USA.
authors.forename: S
authors.initials: S
authors.lastname: Mani
authors.forename: S
authors.initials: S
authors.lastname: McDermott
authors.forename: M
authors.initials: M
authors.lastname: Valtorta
mesh_terms: D000293:Adolescent; D000328:Adult; D000465:Algorithms; D001499:Bayes Theorem; D002648:Child; D002675:Child, Preschool; D003661:Decision Support Techniques; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D008607:Intellectual Disability / Q000175:diagnosis / Q000453:epidemiology* / Q000209:etiology; D007360:Intelligence; D007361:Intelligence Tests / Q000706:statistics & numerical data; D008297:Male; D011247:Pregnancy; D011336:Probability; D011446:Prospective Studies