Research &
Development

Daibeats is advancing the state-of-the-art in ECG-based glucose sensing and clinical AI. As we prioritize external validation and transparency in our progress, we are continuously adding to a growing body of evidence.

July 21, 2025

When Validation Fails: Cross-Institutional Blood Pressure Prediction and the Limits of Electronic Health Record-Based Models

When Validation Fails: Cross-Institutional Blood Pressure Prediction and the Limits of Electronic Health Record-Based Models

External validation remains rare in healthcare machine learning despite being critical for establishing real-world feasibility. We developed an ensemble framework to predict blood pressure from electronic health records, incorporating rigorous data leakage prevention.

Azam, M.B. & Singh, S.I.

Arxiv(Pre-Print) | Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

April 1, 2026

Re-evaluating heart rate variability biomarkers for glucose sensing: the impact of age normalisation and subject-independent validation

Re-evaluating heart rate variability biomarkers for glucose sensing: the impact of age normalisation and subject-independent validation

Heart rate variability (HRV) derived from electrocardiogram (ECG) signals offers a promising non-invasive window into glycemic status; however, existing studies frequently combine distinct glucose measurements and employ validation strategies susceptible to data leakage. Because HRV declines by approximately 3–5% per decade due to age-related autonomic degeneration, absolute HRV values conflate the effects of aging with diabetes-specific autonomic dysfunction. We hypothesised that normalising HRV features using an age-dependent scaling factor would isolate the diabetes-specific component and improve glycemic status estimation.

Azam, M.B. & Singh, S.I.

BMC Medical Informatics and Decision Making