title: Imputation of missing values by integrating neural networks and case-based reasoning.
abstract: Missing values in a medical database present a problem when trying to develop a prediction model for a broad range of patients, if the data are not missing at random. We present a data imputation approach for physiologic parameters that incorporates individualized case information into the imputed values. We replaced missing values in a neonatal intensive care unit (NICU) database with relevant data by integrating aspects of artificial neural networks (ANNs) and case-based reasoning (CBR).
authors.affiliation: Systems and Computer Engineering Department, Carleton University, Ottawa, ON, Canada.
authors.forename: Colleen M
authors.initials: CM
authors.lastname: Ennett
authors.forename: Monique
authors.initials: M
authors.lastname: Frize
authors.forename: C
authors.initials: C
authors.lastname: Walker
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001724:Birth Weight; D003201:Computers; D003627:Data Interpretation, Statistical; D020000:Decision Support Systems, Clinical; D006801:Humans; D007231:Infant, Newborn; D007363:Intensive Care Units, Neonatal / Q000458:organization & administration*; D016571:Neural Networks, Computer; D012107:Research Design; D012984:Software