@inproceedings{ecc5d37f0a904ea7bc24cfbae93f3312,
title = "Prediction of One-Year Transplant-Free Survival after Norwood Procedure Based on the Pre-Operative Data",
abstract = "This paper discusses computational modeling of predictive risk factors for neonates undergoing a Norwood surgical procedure, a multi-stage cardiac procedure that restores functional systemic circulation in patients such as neonates with Hypoplastic Left Heart Syndrome (HLHS). In this model, we apply machine learning based binary classication to 549 cases reported by the Pediatric Heart Networks Single Ventricle Reconstruction Trial. We use neural networks classier to predict risk factors for individual patients undergoing a Norwood procedure for the repair of HLHS. Results indicate that independent risk can be calculated with 85% accuracy and 0.94 area under the receiver operating characteristics curve. This model may help physicians provide counseling for families and medically optimize patients prior to surgery by modifying individual risk factors.",
author = "{Luis Ahumadal}, M. and Jacquelin Peck and Jorge Guerra and Nhue Do and Monesha Gupta and Sharon Ghazarian and Mohamed Rehman and {Jeffrey Jacobs}, P. and Jalali, {And Ali}",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018 ; Conference date: 18-07-2018 Through 21-07-2018",
year = "2018",
month = oct,
day = "26",
doi = "10.1109/EMBC.2018.8513336",
language = "English (US)",
series = "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "3995--3998",
booktitle = "40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018",
}