Prediction of One-Year Transplant-Free Survival after Norwood Procedure Based on the Pre-Operative Data

M. Luis Ahumadal, Jacquelin Peck, Jorge Guerra, Nhue Do, Monesha Gupta, Sharon Ghazarian, Mohamed Rehman, P. Jeffrey Jacobs, And Ali Jalali

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publication40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3995-3998
Number of pages4
ISBN (Electronic)9781538636466
DOIs
StatePublished - Oct 26 2018
Event40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018 - Honolulu, United States
Duration: Jul 18 2018Jul 21 2018

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Volume2018-July
ISSN (Print)1557-170X

Other

Other40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
CountryUnited States
CityHonolulu
Period7/18/187/21/18

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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