Sarangthem Ibotombi Singh

Md Basit Azam, Sarangthem Ibotombi Singh

Non-invasive glucose monitoring remains a critical challenge in the management of diabetes. HRV during sleep shows promise for glucose prediction however, age-related autonomic changes significantly confound traditional HRV analyses. We analyzed 43 subjects with multi-modal data including sleep-stage specific ECG, HRV features, and clinical measurements. A novel age-normalization technique was applied to the HRV features by, dividing the raw values by age-scaled factors. BayesianRidge regression with 5-fold cross-validation was employed for log-glucose prediction. Age-normalized HRV features achieved R2 = 0.161 (MAE = 0.182) for log-glucose prediction, representing a 25.6% improvement over non-normalized features (R2 = 0.132). The top predictive features were hrv rem mean rr age normalized (r = 0.443, p = 0.004), hrv ds mean rr age normalized (r = 0.438, p = 0.005), and diastolic blood pressure (r = 0.437, p = 0.005). Systematic ablation studies confirmed age-normalization as the critical component, with sleep-stage specific features providing additional predictive value. Age-normalized HRV features significantly enhance glucose prediction accuracy compared with traditional approaches. This sleep-aware methodology addresses fundamental limitations in autonomic function assessment and suggests a preliminary feasibility for non-invasive glucose monitoring applications. However, these results require validation in larger cohorts before clinical consideration.

Md Basit Azam, Sarangthem Ibotombi Singh

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.