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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
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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