Abstract #3520
A Ontology-guided Attribute Partitioning Ensemble Learning Model for Early Prediction of Cognitive Deficits using sMRI in Very Preterm Infants
Zhiyuan Li1,2, Hailong Li1,3, Nehal Parikh1,4, Lili He1,4, Adebayo Braimah1, and Jonathan Dillman1,5
1Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States, 2Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, OH, United States, 3The Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States, 4Pediatrics, University of Cincinnati, Cincinnati, OH, United States, 5Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
Synopsis
Between 35-40% of very preterm infants (≤32 weeks’ gestational age) develop cognitive deficits, thereby increasing their risk for poor educational, health, and social outcomes. Timely and accurate identification of infants at risk soon after birth is desirable for early intervention allocation. We proposed a novel Ontology-guided Attribute Partitioning Ensemble Learning (OAP-EL) model using quantitative structural MRI data obtained soon after birth to predict cognitive deficits at 2 years corrected age in very preterm infants.
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