PGP-Clinical-TimeKAN: Prior-Guided Joint Probabilistic Forecasting of Clinical Trajectories
PGP-Clinical-TimeKAN is a trajectory-first framework for joint probabilistic forecasting of multivariate clinical physiology, combining missingness-aware encoders, organ-system priors, and Kolmogorov-Arnold networks The model achieves the lowest RMSE (0.52656) and second-lowest normalized MAE (0.37727) across 13 models on a MIMIC-IV cohort of 6,882 patients and 54,694 windows Relational structure removal causes the largest ablation loss, confirming patient-specific organ-system dependencies are
Analysis
TL;DR
- PGP-Clinical-TimeKAN is a trajectory-first framework for joint probabilistic forecasting of multivariate clinical physiology, combining missingness-aware encoders, organ-system priors, and Kolmogorov-Arnold networks
- The model achieves the lowest RMSE (0.52656) and second-lowest normalized MAE (0.37727) across 13 models on a MIMIC-IV cohort of 6,882 patients and 54,694 windows
- Relational structure removal causes the largest ablation loss, confirming patient-specific organ-system dependencies are critical to performance
- Probabilistic forecasting shows empirical coverage of 0.533/0.831/0.958 for nominal 50%/80%/95% intervals, with marginal NLL of 0.66380 and CRPS of 0.27301
- A trajectory-derived risk score (AUROC 0.603) underperforms a dedicated GRU-D classifier (AUROC 0.650), revealing that accurate physiology forecasting does not guarantee calibrated event detection
Why It Matters
This work bridges temporal sequence modeling and clinical decision support by demonstrating that joint probabilistic trajectory forecasting can serve as an inspectable intermediate task for understanding patient deterioration. However, the gap between forecasting accuracy and classification performance highlights a critical lesson for AI practitioners: strong predictive modeling of physiological signals does not automatically translate into reliable clinical event detection, necessitating dedicated downstream classifiers for deployment-critical tasks.
Technical Details
- Architecture: Combines missingness-aware temporal encoders, a soft organ-system prior encoding physiological relationships, patient-specific relational graphs, nonlinear Kolmogorov-Arnold message passing, and a low-rank multivariate Student-t output head for probabilistic forecasting
- Dataset & Evaluation: Frozen MIMIC-IV-derived cohort with 6,882 patients and 54,694 sliding windows; 24-hour history windows used to forecast 6 hours ahead; evaluated across 5 random seeds and 13 baseline models
- Performance: Reduces MAE by 0.52% over deterministic TimeKAN; ablation studies show relational structure is the most impactful component, while increasing covariance rank improves joint likelihood without affecting point accuracy
- Limitation: The trajectory-derived risk score (AUROC 0.603) lags behind a purpose-built GRU-D classifier (AUROC 0.650), indicating a performance ceiling for using forecasting as a proxy for classification
Industry Insight
- Healthcare AI teams should treat trajectory forecasting as a valuable interpretability tool and representation learning objective, but not assume it substitutes for task-specific classifiers in production clinical alerting systems
- The Kolmogorov-Arnold network component demonstrates growing practical utility beyond standard MLPs/RNNs for modeling complex nonlinear physiological relationships, warranting further exploration in time-series domains
- The coverage-accuracy tradeoff observed (empirical coverage slightly below nominal at 95%) suggests probabilistic models in clinical settings require careful calibration tuning before deployment, as overconfident uncertainty estimates could undermine clinician trust
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