Temporally consistent survival prediction for non-uniform longitudinal data

Traditional survival prediction models use a patient's covariates at a single time point to estimate the time until a specific event occurs, such as death or hospital readmission. However, in many longitudinal datasets, patient covariates are recorded at multiple time points, typically with varying intervals. Our objective is to learn a survival prediction model by training on longitudinal datasets with non-uniform time intervals between covariate measurements, both within and across patient trajectories.

We propose a new algorithm, Temporally Consistent Multi-Task Logistic Regression (TC-MTLR), which incorporates concepts from distributional reinforcement learning to model survival outcomes. Unlike existing dynamic survival prediction algorithms, TC-MTLR is designed to leverage the non-uniformity of longitudinal measurements. We evaluate this method against two standard and two dynamic survival prediction algorithms across three short and three long longitudinal datasets, including two related to healthcare.

On short datasets, TC-MTLR achieves top performance in Concordance Index (C-Index) and Uncensored Mean Average Error (MAE-Uncensored) while displaying mixed results according to Integrated Brier Score (IBS) and Pseudo-Observable MAE (MAE-PO). However, on long datasets, TC-MTLR achieves similar C-Index performance as the other survival predictions methods while outperforming them according to MAE-PO and achieving top performance according to MAE-Uncensored and IBS.

TC-MTLR effectively utilizes the non-uniform temporal structure of longitudinal datasets, offering a competitive and often superior alternative to existing survival prediction models.