Hemorrhage remains the leading cause of preventable trauma death, with traditional vital signs failing to detect blood loss until 25-30% volume depletion occurs. Compensatory Reserve Measurement (CRM) enables earlier hemorrhage detection but current estimation methods force a tradeoff between performance and interpretability. We present the first Vision Transformer (ViT) for CRM estimation that achieves both superior accuracy compared to previous models and mechanistic explainability from arterial blood pressure (ABP) waveforms. Using data from 208 human subjects who underwent progressive lower body negative pressure, we developed a single-layer ViT that processes 20-second waveform segments as token sequences. Rigorous 10-fold cross-validation compared the ViT against state of-the-art Convolutional Neural Network (CNN) and manual feature-based models using identical train-validation-test splits. With all models undergoing equivalent Optuna hyperparameter optimization, the ViT achieved higher R2 (0.80 vs 0.77) with fold-level paired t-test p = 0.052 (N = 10) and subject-level p = 0.008 (N = 208). The ViT also demonstrated superior robustness to signal corruption, with the CNN's performance degrading progressively faster under increasing noise and sample dropout. Attention analysis revealed learned patterns converging with established physiological knowledge, prioritizing half-decay and dicrotic notch regions identified as critical by manual feature extraction from the ABP. The model shifted from focused attention at high CRM to distributed monitoring at low CRM, matching known hemodynamics near decompensation. Ablation experiments confirmed half-decay regions as functionally critical. This work bridges the performance-interpretability tradeoff, providing the first interpretable deep learning approach for hemorrhage monitoring and CRM estimation.
Tibbs et al. (Thu,) studied this question.