Abstract #3520

A Ontology-guided Attribute Partitioning Ensemble Learning Model for Early Prediction of Cognitive Deficits using sMRI in Very Preterm Infants

Zhiyuan Li^1,2, Hailong Li^1,3, Nehal Parikh^1,4, Lili He^1,4, Adebayo Braimah¹, and Jonathan Dillman^1,5

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.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.