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Machine Learning to Predict Physiological Age Using Features Extracted From Arterial Blood Pressure (ABP) Waveforms (Case No. 2025-264)

Summary: UCLA researchers in the Department of Anesthesiology have introduced a novel machine learning model that accurately predicts physiological aging by analyzing features extracted from patients’ arterial blood pressure (ABP) waveforms. This approach enables efficient, data-driven assessment of biological aging in clinical settings to facilitate...

Published: 10/23/2025 | Inventor(s): Maxime Cannesson, Ravi Pal

Keywords(s): Aging, Anesthesiology, biomedical sensors, clinical care methodology, clinical decision support, clinical outcome, Clinical tool, Computer-Aided Diagnosis, consumer health products, Digital Health, digital health system, Fatigue (Medical) , Health and fitness, health risk assessment, Machine Learning, machine learning modeling, Medical Device, Medical diagnostics, Monitoring (Medicine), personalized medicine, Preventive Medicine, public health monitoring, Smart medical device, Structural health monitoring, Telemedicine

Category(s): Diagnostic Markers, Diagnostic Markers > Aging, Life Science Research Tools, Medical Devices