Clinical-Grade Blood Pressure Prediction in ICU Settings: An Ensemble Framework with Uncertainty Quantification and Cross-Institutional Validation

• URL: http://arxiv.org/abs/2507.19530v1
• Date: Mon, 21 Jul 2025 11:15:33 GMT
• Title: Clinical-Grade Blood Pressure Prediction in ICU Settings: An Ensemble Framework with Uncertainty Quantification and Cross-Institutional Validation
• Abstract summary: Blood pressure (BP) monitoring is critical in in tensive care units (ICUs) where hemodynamic instability can rapidly progress to cardiovascular collapse.<n>Current machine learning (ML) approaches suffer from three limitations: lack of external validation, absence of uncertainty quantification, and inadequate data leakage prevention.<n>This study presents the first comprehensive framework with novel algorithmic leakage prevention, uncertainty quantification, and cross-institutional validation.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Blood pressure (BP) monitoring is critical in in tensive care units (ICUs) where hemodynamic instability can rapidly progress to cardiovascular collapse. Current machine learning (ML) approaches suffer from three limitations: lack of external validation, absence of uncertainty quantification, and inadequate data leakage prevention. This study presents the first comprehensive framework with novel algorithmic leakage prevention, uncertainty quantification, and cross-institutional validation for electronic health records (EHRs) based BP pre dictions. Our methodology implemented systematic data leakage prevention, uncertainty quantification through quantile regres sion, and external validation between the MIMIC-III and eICU databases. An ensemble framework combines Gradient Boosting, Random Forest, and XGBoost with 74 features across five physiological domains. Internal validation achieved a clinically acceptable performance (for SBP: R^2 = 0.86, RMSE = 6.03 mmHg; DBP: R^2 = 0.49, RMSE = 7.13 mmHg), meeting AAMI standards. External validation showed 30% degradation with critical limitations in patients with hypotensive. Uncertainty quantification generated valid prediction intervals (80.3% SBP and 79.9% DBP coverage), enabling risk-stratified protocols with narrow intervals (< 15 mmHg) for standard monitoring and wide intervals (> 30 mmHg) for manual verification. This framework provides realistic deployment expectations for cross institutional AI-assisted BP monitoring in critical care settings. The source code is publicly available at https://github.com/ mdbasit897/clinical-bp-prediction-ehr.

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