Research Article: Feature-token clinical transformer for 24-h landmark mortality risk stratification in critically ill patients with heart failure: development and multicenter external validation

Abstract:
The first 24?h after ICU admission in critically ill patients with heart failure yield heterogeneous EHR evidence across vital signs, laboratory tests, treatments, organ support, and nurse-charted monitoring. Conventional scores and flat-vector ML models may not preserve individual variable identity or context. We developed FTCT, encoding each predictor recorded during the first 24?h after ICU admission as a clinically tagged token (value, missingness, identity, group). MIMIC-IV (2008–2019) served for development, validation, and internal testing; eICU-CRD (2014–2015) served for external validation. Included were adults with heart failure, a complete 24-h ICU observation window, and known discharge status. The endpoint was in-hospital all-cause death. MIMIC-IV included 11,156 stays and 1,484 deaths, partitioned into training ( n =?7,809), validation ( n =?1,673), and internal test ( n =?1,674) sets. eICU-CRD included 9,837 stays and 1,319 deaths. FTCT achieved an AUROC of 0.849 (95% CI, 0.835–0.863) internally and 0.804 (0.788–0.820) externally. Patient-paired AUROC differences vs. XGBoost were 0.027 (0.011–0.043) internally and 0.032 (0.014–0.050) externally. A matched FT-Transformer-style baseline achieved an external AUROC of 0.781 (0.760–0.801). In eICU-CRD, FTCT achieved an AUPRC of 0.296 (0.278–0.314), Brier score of 0.091 (0.085–0.097), and expected calibration error of 0.036 (0.030–0.042). In a post hoc cohort-specific descriptive operating analysis targeting approximately 85% specificity, external sensitivity was 0.662 (0.636–0.687), positive predictive value 0.406 (0.385–0.427), negative predictive value 0.942 (0.937–0.947), and F1 score 0.503. Removing feature-token encoding produced the largest ablation loss in external AUROC (?0.031). FTCT combines established feature-token Transformer modeling with clinical-group embeddings, explicit missingness representation, and group-aware pooling for 24-h landmark mortality risk stratification from structured EHR data recorded during the first 24?h after ICU admission. Its external validation and ablation results support prospective investigation, but do not yet demonstrate clinical utility, workflow integration, admission-time applicability, or autonomous use.

Introduction:
The first 24?h after ICU admission in critically ill patients with heart failure yield heterogeneous EHR evidence across vital signs, laboratory tests, treatments, organ support, and nurse-charted monitoring. Conventional scores and flat-vector ML models may not preserve individual variable identity or context.

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