Benchmarking Supervised Machine Learning Models for the Classification of Primary Graft Dysfunction

Purpose: Primary Graft Dysfunction (PGD) contributes to early post-lung transplantation (LTx) morbidity and mortality. Although chest X-ray and PaO2/FiO2 ratio are evaluated in PGD grading, multiple features interact and impact the development of PGD, and how these should be used in making clinical decisions remain doubtful. Thus, we aim to develop machine learning (ML) pipelines to predict patient classes based on our temporal PGD classification and compare their performance.

Source: The Journal of Heart and Lung Transplantation - April 1, 2024 Category: Transplant Surgery Authors: , A. Khan, J. Van Slambrouck, C. Vandervelde, C. Vanluyten, X. Jin, A. Barbarossa, J. Kaes, L. Schoonjans, R. Vos, D. Van Raemdonck, P. Carmeliet, L. Ceulemans Source Type: research