Subject: 5P-Medicine; digital health; mobile bio-sensing for medicine; patient empowerment technologies; artificial intelligence; cardiovascular diseases
Year: 2021
Type: Article
Title: Mobile 5P-medicine approach for cardiovascular patients
Author: Miguel Pires, Ivan
Author: Vitaliyivna Denysyuk, Hanna
Author: Vanessa Villasana, María
Author: Sá, Juliana
Author: Lameski, Petre
Author: Chorbev, Ivan
Author: Zdravevski, Eftim
Author: Trajkovik, Vladimir
Author: Francisco Morgado, José
Author: M Garcia, Nuno
Abstract: Medicine is heading towards personalized care based on individual situations and conditions. With smartphones and increasingly miniaturized wearable devices, the sensors available on these devices can perform long-term continuous monitoring of several user health-related parameters, making them a powerful tool for a new medicine approach for these patients. Our proposed system, described in this article, aims to develop innovative solutions based on artificial intelligence techniques to empower patients with cardiovascular disease. These solutions will realize a novel 5P (Predictive, Preventive, Participatory, Personalized, and Precision) medicine approach by providing patients with personalized plans for treatment and increasing their ability for self-monitoring. Such capabilities will be derived by learning algorithms from physiological data and behavioral information, collected using wearables and smart devices worn by patients with health conditions. Further, developing an innovative system of smart algorithms will also focus on providing monitoring techniques, predicting extreme events, generating alarms with varying health parameters, and offering opportunities to maintain active engagement of patients in the healthcare process by promoting the adoption of healthy behaviors and well-being outcomes. The multiple features of this future system will increase the quality of life for cardiovascular diseases patients and provide seamless contact with a healthcare professional.
Publisher: MDPI
Relation: Sensors
Identifier: oai:repository.ukim.mk:20.500.12188/26129
Identifier: http://hdl.handle.net/20.500.12188/26129
