GraftIQ: Hybrid multi-class neural network integrating clinical insight for multi-outcome prediction in liver transplant recipients

GraftIQ: Hybrid multi-class neural network integrating clinical insight for multi-outcome prediction in liver transplant recipients

Abstract Liver transplant recipients (LTRs) are at risk of graft injury, leading to cirrhosis and reduced survival. Liver biopsy, the diagnostic gold standard, is invasive and risky. We developed a hybrid multi-class neural network (NN) model, ‘GraftIQ,’ integrating clinician expertise for non-invas...

Full description

Saved in:
Bibliographic Details
Main Authors: Divya Sharma, Neta Gotlieb, Daljeet Chahal, Joseph C. Ahn, Bastian Engel, Richard Taubert, Eunice Tan, Lau Kai Yun, Sara Naimimohasses, Ankit Ray, Yoojin Han, Sara Gehlaut, Maryam Shojaee, Surabie Sivanendran, Maryam Naghibzadeh, Amirhossein Azhie, Sareh Keshavarzi, Kai Duan, Leslie Lilly, Nazia Selzner, Cynthia Tsien, Elmar Jaeckel, Wei Xu, Mamatha Bhat
Format: Article
Language:English
Published: Nature Portfolio 2025-05-01
Series:Nature Communications
Online Access:https://doi.org/10.1038/s41467-025-59610-8
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://doi.org/10.1038/s41467-025-59610-8

Similar Items