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Imputation of missing values by integrating neural networks and case-based reasoning
Colleen M Ennett1, Monique Frize, C Walker
1Systems and Computer Engineering Department, Carleton University, Ottawa, ON, Canada.
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).
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