Predicting future organ dysfunction in Intensive Care Unit (ICU) patients is critical for early clinical intervention, yet existing machine learning approaches have largely treated the Sequential Organ Failure Assessment (SOFA) score as an input to binary mortality prediction rather than as a continuous clinical outcome in its own right. We investigate the extent to which a Temporal Convolutional Net work (TCN) can predict next-day SOFA scores from multivariate ICU time-series data extracted from MIMIC-IV, characterise the relative contribution of each organ system to total SOFA variance and deterioration, and identify distinct trajectory patterns across ICU stays. A residual TCN trained on three-day sliding windows achieved a five-fold cross-validation R2 of 0.740 +- 0.013 and MAE of 1.431 +- 0.022, outperforming a naive persistence baseline on RMSE and R2. SHAP interpretability analysis revealed that the model functions primarily as a severity-anchoring mechanism rather than a true sequence model, with predictions dominated almost entirely by the most recent observation day. Cardiovascular dysfunction emerged as the strongest discriminator of both cross-sectional severity and acute deterioration, and unsupervised trajectory clustering identified two clinically meaningful phenotypes, an improving group (58.9%) and a persistently severe group (41.1%), differentiated by cardiovascular, hepatic, coagulation, and renal involvement. We conclude that TCNs can extract meaningful predictive signal from ICU physiological data, but that short input windows and complete-case selection bias currently limit their clinical utility, motivating future work on longer input horizons, alternative missing-data strategies, and external validation.
Figures & tables
MAE
RMSE
R2
CV mean ± SD
1.431±0.022
1.882±0.027
0.740±0.013
Holdout
1.462
1.906
0.715
Persistence baseline
1.401
1.933
0.707
Table 1: TCN performance across five-fold ICU stay-level cross-validation, single holdout evaluation, and the naïve persistence baseline.
Figure 1: MAE, RMSE, and R2 across five cross-validation folds, with mean performance shown as a dashed line.
Figure 2: Predicted vs actual SOFA Evaluation (left), error distribution (centre), and training history (right).
Figure 3: SHAP feature importance (left) and temporal SHAP heatmap by feature and time step (right).
Figure 4: SOFA subscore variance contribution (left) and Pearson correlation with total SOFA score (right).
Table 4: TCN regression performance stratified by SOFA severity band (supports Section 4.2 ).
Figure 8: Predicted vs actual SOFA trajectories for six sampled ICU admissions, illustrating the model’s smoothing bias under rapid deterioration or recovery.
Figure 9: Silhouette score by number of clusters (left) and elbow plot of inertia (right), supporting Section 4.6 .
Figure 10: Average organ system contribution to SOFA score by severity band, supporting Section 4.5 .
Figure 11: Mean subscore difference between deteriorating and stable days by organ system, supporting Section 4.5 .
Figure 12: Box plot of SOFA subscore distributions by organ system, supporting Section 4.5 .
Organ system
Variables (source tables)
Cardiovascular
MAP, vasopressor rates (chartevents, inputevents)
Respiratory
FiO 2 , PaO 2 (chartevents, labevents)
Neurological
GCS components (chartevents)
Renal
Serum creatinine (labevents)
Hepatic
Total bilirubin (labevents)
Coagulation
Platelet count (labevents)
Appendix
Table 5: SOFA organ system variables and their MIMIC-IV source tables, supporting Section 3.2 .
Computational Precision Health UC Berkeley, UCSF · Samsung Advanced Institute of Technology (SAIT) · Computational Precision Health UC Berkeley, UCSF[cs.LG]