GTsurvival: A Hybrid GCN-Neural Decision Tree Model for Restricted Mean Survival Time Prediction with Complex Censored Data

Abstract

Chronic diseases, particularly those with progressive neurological impairment, present a significant challenge in healthcare due to their impact on millions globally and the limited availability of effective therapies. Addressing this challenge requires innovative approaches, such as leveraging individuals' genetic features for early intervention and treatment strategies. Due to the irregular intervals of patient visits, clinical data typically appear as censored, necessitating advanced analytical methods. Thus, this study introduces GTsurvival, a novel network architecture that combines graph convolutional networks (GCN) with a neural decision tree, providing promising advancements in disease prediction. GTsurvival utilizes restricted mean survival time (RMST) as pseudo-observations and directly connects them with baseline variables. Through the joint simulation of RMST, GTsurvival can effectively utilize shared information and enhance its predictive ability for patients' future survival status. Firstly, GTsurvival is introduced to handle complex censored data, emphasizing the crucial role of graphs utilized in GCNs for processing related information among samples. Secondly, the neural decision tree within GTsurvival enhances decision-making by mitigating uncertainty at split nodes, effectively minimizing the global loss function and optimizing survival analysis in high-dimensional datasets. Thirdly, evaluations on simulated datasets and a real-world neurodegenerative disease cohort verify that the proposed GTsurvival method surpasses existing approaches. This superiority is partly attributed to the inclusion of a generalized score test during feature selection, which helps capture variants associated with disease progression.

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