LiveHeart: AI-Augmented Lifestyle Habit Monitoring System for Decision Making in Digital Care Pathway
Sze Mei, Ashley Wang; Keikhosrokiani, Pantea; Isomursu, Minna (2023-10-15)
R. Piskac c/o Redaktion Sun SITE, Informatik V, RWTH Aachen
15.10.2023
Sze Mei, A. W., Keikhosrokiani, P. & Isomursu, M. (2023). LiveHeart: AI-Augmented Lifestyle Habit Monitoring System for Decision Making in Digital Care Pathway. In J. Kasurinen & T. Päivärinta (Eds.), Proceedings of the Annual Symposium of Computer Science 2023. Retrieved from https://ceur-ws.org/Vol-3506/paper01.pdf
https://creativecommons.org/licenses/by/4.0/
© 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
https://creativecommons.org/licenses/by/4.0/
© 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
https://creativecommons.org/licenses/by/4.0/
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202312013478
https://urn.fi/URN:NBN:fi:oulu-202312013478
Tiivistelmä
Abstract
The advancement of interoperable digital technology has had remarkable impacts on society especially in the healthcare area. Ischemic heart disease is a major cause of disability and premature death in many parts of the world that can still be mitigated through lifestyle changes. While the existing mHealth solutions that encourage users to maintain healthy lifestyle habits do exist, they lack the reliable advice of healthcare professionals to monitor patient’s lifestyle habits remotely. The process of manually collecting patient data for analysis and clinical testing can also be time-consuming for healthcare professionals. Therefore, this study proposed a web-based information system to monitor lifestyle habits and habit-change of people prone to heart disease. The system collects lifestyle habit data from a smartwatch and smartphone. Then, the system utilizes machine learning techniques to classify the patient’s lifestyle habit data such as diet, exercise, and sleep. Furthermore, the system is used to upload echocardiograms during the echocardiography test in the hospitals and receive the echocardiography results. The data analytical results are visualized on an intelligent dashboard that can be viewed by doctors using the web application. The system is expected to support doctors in decision-making for digital care pathways in order to provide timely intervention for lifestyle modifications.
The advancement of interoperable digital technology has had remarkable impacts on society especially in the healthcare area. Ischemic heart disease is a major cause of disability and premature death in many parts of the world that can still be mitigated through lifestyle changes. While the existing mHealth solutions that encourage users to maintain healthy lifestyle habits do exist, they lack the reliable advice of healthcare professionals to monitor patient’s lifestyle habits remotely. The process of manually collecting patient data for analysis and clinical testing can also be time-consuming for healthcare professionals. Therefore, this study proposed a web-based information system to monitor lifestyle habits and habit-change of people prone to heart disease. The system collects lifestyle habit data from a smartwatch and smartphone. Then, the system utilizes machine learning techniques to classify the patient’s lifestyle habit data such as diet, exercise, and sleep. Furthermore, the system is used to upload echocardiograms during the echocardiography test in the hospitals and receive the echocardiography results. The data analytical results are visualized on an intelligent dashboard that can be viewed by doctors using the web application. The system is expected to support doctors in decision-making for digital care pathways in order to provide timely intervention for lifestyle modifications.
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