Framework for Monitoring Peruvian Patients with Hypertension using a Smartwatch and GPT

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Indexed keywords Sustainable Development Goals SciVal Topics Abstract Hypertension has been a silent disease that has affected a large part of the world population; in 2022, 5.5 million cases were registered in Peru. Current treatments show an inadequate control of this disease. Therefore, a framewo...

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Detalles Bibliográficos
Autores: Rosales, Miguel, Huacacolque, Enzo, Castillo-Sequera, Jose Luis, Wong, Lenis
Formato: artículo
Fecha de Publicación:2024
Institución:Universidad Peruana de Ciencias Aplicadas
Repositorio:UPC-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorioacademico.upc.edu.pe:10757/676107
Enlace del recurso:http://hdl.handle.net/10757/676107
Nivel de acceso:acceso embargado
Materia:Hypertension, Framework, Blood pressure
Smartwatch, GPT, Blood pressure monitoring
Descripción
Sumario:Indexed keywords Sustainable Development Goals SciVal Topics Abstract Hypertension has been a silent disease that has affected a large part of the world population; in 2022, 5.5 million cases were registered in Peru. Current treatments show an inadequate control of this disease. Therefore, a framework is proposed to build an application for remote monitoring of hypertensive patients using technologies such as smartwatches and artificial intelligence of GPT, considering the diagnostic methodologies of hypertension used in the world, physiological variables and the implementation of GPT-4 as an assistant for the correct treatment of hypertension. The methodology was followed: selection of measurement techniques, selection of physiological variables, selection of the smartwatch model, implementation of GPT-4 and construction of a mobile application. The experimentation had two scenarios: (a) use of the traditional model and (b) using the proposed method. The results of the experimentation showed that the time to measure and record blood pressure and heart rate (TMR) was 44.44% faster with the app. The medical diagnosis time (TMD) was 80% more efficient than the traditional method. In addition, in the expert judgment evaluation, patients and cardiologists rated the solution with 4.2 and 4.1 points respectively, valuing it as 'agree' in use of the proposed solution.
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