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<p>Reliable ICU mortality prediction requires capturing physiologic evolution over time and interpreting these patterns in clinically meaningful ways. However, existing methods often focus on individual variables or isolated time points, limiting their ability to represent related physiologic states and temporal progression. We developed a group-based temporal interpretation framework by applying GroupSegment-SHAP to characterize the evolving contributions of physiologic groups before ICU mortality. Using MIMIC-IV, we evaluated prediction models across 24-, 48-, and 72-hour death-aligned lookback windows and selected Mamba, a selective state-space model, as the interpretation backbone because it showed comparatively strong and stable performance. Clinical variables were reorganized into physiologic groups, and Shapley values from the trained Mamba model were used to quantify group-wise temporal importance. Group-level interpretation identified hemodynamic, neurologic, cardiopulmonary, and temperature-related groups as major mortality predictors. Temporal analysis showed that 24-hour trajectories emphasized acute cardiopulmonary, metabolic, hematologic, and acid-base/respiratory abnormalities, whereas 48- and 72-hour trajectories additionally captured hemodynamic, renal-output, neurologic, and hepatic differences. Cardiopulmonary importance increased 8 to 30&#xa0;h before peak group-level importance, when non-survivors showed lower normal-range proportions than survivors by up to 17.5% points. This analysis shifts interpretation from static variable-level importance to temporal trajectories of physiologic groups.</p>

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Temporal interpretation of physiologic group contributions for ICU mortality prediction

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月21日

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  • Jinwoong Kim,
  • Yeeun Kim,
  • Sangjin Park

摘要

Reliable ICU mortality prediction requires capturing physiologic evolution over time and interpreting these patterns in clinically meaningful ways. However, existing methods often focus on individual variables or isolated time points, limiting their ability to represent related physiologic states and temporal progression. We developed a group-based temporal interpretation framework by applying GroupSegment-SHAP to characterize the evolving contributions of physiologic groups before ICU mortality. Using MIMIC-IV, we evaluated prediction models across 24-, 48-, and 72-hour death-aligned lookback windows and selected Mamba, a selective state-space model, as the interpretation backbone because it showed comparatively strong and stable performance. Clinical variables were reorganized into physiologic groups, and Shapley values from the trained Mamba model were used to quantify group-wise temporal importance. Group-level interpretation identified hemodynamic, neurologic, cardiopulmonary, and temperature-related groups as major mortality predictors. Temporal analysis showed that 24-hour trajectories emphasized acute cardiopulmonary, metabolic, hematologic, and acid-base/respiratory abnormalities, whereas 48- and 72-hour trajectories additionally captured hemodynamic, renal-output, neurologic, and hepatic differences. Cardiopulmonary importance increased 8 to 30 h before peak group-level importance, when non-survivors showed lower normal-range proportions than survivors by up to 17.5% points. This analysis shifts interpretation from static variable-level importance to temporal trajectories of physiologic groups.