医学
可穿戴计算机
血压
心理干预
重症监护医学
可穿戴技术
医疗保健
医疗急救
人工智能
机器学习
内科学
嵌入式系统
计算机科学
护理部
经济
经济增长
作者
Anubhuti Juyal,Shradha Bisht,Mamta F. Singh
标识
DOI:10.1097/mbp.0000000000000711
摘要
Hypertension, a widespread cardiovascular issue, presents a major global health challenge. Traditional diagnosis and treatment methods involve periodic blood pressure monitoring and prescribing antihypertensive drugs. Smart technology integration in healthcare offers promising results in optimizing the diagnosis and treatment of various conditions. We investigate its role in improving hypertension diagnosis and treatment effectiveness using machine learning algorithms for early and accurate detection. Intelligent models trained on diverse datasets (encompassing physiological parameters, lifestyle factors, and genetic information) to detect subtle hypertension risk patterns. Adaptive algorithms analyze patient-specific data, optimizing treatment plans based on medication responses and lifestyle habits. This personalized approach ensures effective, minimally invasive interventions tailored to each patient. Wearables and smart sensors provide real-time health insights for proactive treatment adjustments and early complication detection.
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