Treatment-Informed Continuous-Time Dynamics with Structural Output Coherence for Multi-Task Chronic Kidney Disease Progression Prediction
TL;DR
TIDE, a Treatment-Informed Dynamics Encoder, a diagonal continuous-time state space uses the true inter-observation interval as its integration step and lets the exogenous intervention channel modulate the decay eigenvalues, so an intervention alters the rate law rather than the level.
Abstract
Predicting chronic kidney disease progression from longitudinal records is a multi-task sequence problem: the targets are correlated, the series is irregularly sampled and sparsely observed, and standard learners fit each target independently and can therefore return mutually contradictory estimates for the same instance. We present TIDE, a Treatment-Informed Dynamics Encoder. A diagonal continuous-time state space uses the true inter-observation interval as its integration step and lets the exogenous intervention channel modulate the decay eigenvalues, so an intervention alters the rate law rather than the level. A structural coherence layer emits one latent trajectory from which five of the seven targets are derived analytically, and cause-specific hazards give the two time-to-event targets. On a 6 000-patient simulated longitudinal cohort with 107 978 visits, TIDE was compared with six baselines including gradient boosting, random forest, XGBoost and CatBoost under one pipeline, identical splits and an equal tuning budget. TIDE improved rapid-progression discrimination to 0.810 against 0.769 for the best tabular model (difference +0.040 (95% CI +0.020 to +0.062, P < 0.001)), reduced future-eGFR error from 2.48 to 2.16 mL/min/1.73 m2, raised the concordance index to 0.939, and cut internally contradictory predictions from 0.061 to 0.0005. Annual-decline regression remained below the boosted ensembles and is reported as such.