AI-powered joint model of longitudinal and survival outcomes with various survival loss functions

Music City Center 

Improving prediction accuracy in precision medicine is critical for identifying and treating patients at risk in a timely manner. Accounting for temporal dynamics between variables through jointly modelling longitudinal data and data increases time-to-event predictions. However, parametric assumptions in both the longitudinal and survival sub-models and computational burden in integrating a large number of random effects of multivariate longitudinal data are limitations of traditional joint models. In this study, we propose a deep-learning joint modeling architecture using Kolmogorov-Arnold Networks: JM-KAN.
We utilized various survival loss functions such as Cox proportional hazards (PH) and non-proportional Cox-Time in building a survival sub-model for JM-KAN. We have utilized two clinical datasets: 1) 2711 unique patients with Mild Cognitive Impairment (MCI) without any prior diagnosis of Alzheimer's disease (AD) from the National Alzheimer's Coordinating Center (NACC), to predict their disease progression from MCI to AD, and 2) 32,525 liver transplantation (LT) recipients with major adverse cardiovascular events (MACE) diagnosis within 90 days post-LT to track their death following MACE. We also utilized 100 simulated datasets of 1000 subjects, with PH, unspecified interactions, and non-PH scenarios.
Comparing the KAN-based survival sub-model to existing survival methods such as random survival forests, probability mass function (Deephit) demonstrated that Cox PH model showed high discrimination in PH scenario and Cox-Time model showed enhanced overall performance. Cox-Time model also showed superiority in death prediction in OPTN data. Joining these Cox PH (CPH) and Cox-Time (CT) sub-models to dynamic longitudinal predictions, we have found that JM-KAN-CT had the highest discrimination performance for all three simulation scenarios (integrated area under curve (AUC) 0.912-0.921) as well as calibration (integrated Brier Score (BS) 0.057-0.064). JM-KAN-CPH also showed comparable calibration as JM-KAN-CT under the PH scenario. In clinical datasets, JM-KAN-CPH showed superiority in dynamic prediction of both longitudinal covariates and survival probability when compared to existing methods such as Deepsurv, MFPCCox, and MATCH-net using the NACC dataset. Similarly, JM-KAN-CT had the highest iAUC (0.669) and the lowest iBS (0.171) when compared to the same models using the OPTN data.
We can conclude that JM-KAN performed well in discrimination and calibration, although the computational burden such as run time and requiring the whole data for analysis remains a challenge. In the future, fast approximation to loss function as well as integration of stochastic methods may be warranted.

AI

Joint modeling

Neural Networks

Prognosis

Prediction 

Section on Statistical Computing