09: Temporal Nested LASSO for Timely ICU Outcome Prediction with Longitudinal Electronic Health Records

Thomas M. Menino Convention & Exhibition Center 

Accurate ICU outcome prediction increasingly relies on longitudinal electronic health record data, yet clinical decisions require timely and interpretable models that avoid dependence on late-arriving measurements. We propose a Temporal Nested LASSO (TNL) framework for hierarchical variable selection in longitudinal settings, enforcing an "earlier-before-later" structure so that later measurements enter the model only if earlier information is retained. The method simultaneously enables group-level selection of time windows and within-window sparsity among predictors, yielding compact and temporally consistent models. Optional time-dependent penalty weights further prioritize early, clinically actionable predictors. We extend the framework to generalized linear models to accommodate non-Gaussian ICU outcomes such as mortality. Simulation studies and applications to multi-center ICU data demonstrate that TNL achieves a favorable balance between predictive accuracy, sparsity, and timeliness compared with existing nested and group-structured penalties, supporting earlier and more interpretable clinical prediction.

Nested LASSO

Hierarchical variable selection

Timely prediction

ICU outcomes

GLM 

Biopharmaceutical Section