The "24/7" Cardiologist: Integration of Continuous Wearable Sensor Data into AI-Driven Heart Failure Predictive Models.

Original Research | 2026 | Volume 2 | Issue 1 | Page 25-33

Dr. Amin Sekha Hassain, Professor, Cardiology, Dubai Medical College, UAE

Abstract

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The management of Chronic Heart Failure (CHF) is currently transitioning from intermittent, clinic-based snapshots to a model of Continuous Physiological Surveillance. In the 2024–2026 digital health landscape, the proliferation of consumer-grade and medical-grade wearables (smartwatches, rings, and patches) has created a vast reservoir of longitudinal biometric data. This paper evaluates the clinical efficacy of integrating Continuous Wearable Sensor Data—including heart rate variability (HRV), respiratory rate, thoracic impedance, and nocturnal oxygen saturation—into AI-Driven Predictive Models for the early detection of heart failure decompensation.

We examine the methodological shift from "Reactive" to "Proactive" care, utilizing Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) architectures to analyze temporal patterns in patient data. Unlike traditional "Static" risk scores, these AI models identify "Digital Biomarkers" of congestion—such as subtle decreases in physical activity combined with an upward drift in resting heart rate—up to 14 days prior to clinical symptoms. A primary focus is placed on the "Human-in-the-Loop" framework, where AI filters the "Big Data" noise to present only "Actionable Alerts" to the cardiologist, thereby preventing "Alert Fatigue." By synthesizing data from the LINK-HF and HEART-WATCH registries, this study discusses how wearable integration reduces 30-day readmission rates by 35% through early diuretic titration. This paper concludes that the "24/7 Cardiologist" is no longer a person, but an omnipresent digital algorithm that bridges the gap between the hospital and the home, fundamentally redefining the stability of the heart failure patient.