Predicting Medical Interventions From Vital Parameters: Towards a Decision Support System for Remote Patient Monitoring

Gontarska, K., Wrazen, W., Beilharz, J., Schmid, R., Thamsen, L. and Polze, A. (2021) Predicting Medical Interventions From Vital Parameters: Towards a Decision Support System for Remote Patient Monitoring. In: 19th Conference on Artificial Intelligence in Medicine (AIME'21), 15-18 Jun 2021, pp. 293-297. ISBN 9783030772109 (doi: 10.1007/978-3-030-77211-6_33)

[img] Text
268159.pdf - Accepted Version

312kB

Abstract

Cardiovascular diseases and heart failures in particular are the main cause of non-communicable disease mortality in the world. Constant patient monitoring enables better medical treatment as it allows practitioners to react on time and provide the appropriate treatment. Telemedicine can provide constant remote monitoring so patients can stay in their homes, only requiring medical sensing equipment and network connections. A limiting factor for telemedical centers is the amount of patients that can be monitored simultaneously. We aim to increase this amount by implementing a decision support system. This paper investigates a machine learning model to estimate a risk score based on patient vital parameters that allows sorting all cases every day to help practitioners focus their limited capacities on the most severe cases. The model we propose reaches an AUCROC of 0.84, whereas the baseline rule-based model reaches an AUCROC of 0.73. Our results indicate that the usage of deep learning to improve the efficiency of telemedical centers is feasible. This way more patients could benefit from better health-care through remote monitoring .

Item Type:Conference Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Thamsen, Dr Lauritz
Authors: Gontarska, K., Wrazen, W., Beilharz, J., Schmid, R., Thamsen, L., and Polze, A.
College/School:College of Science and Engineering > School of Computing Science
ISSN:0302-9743
ISBN:9783030772109
Published Online:08 June 2021
Copyright Holders:Copyright © 2021 Springer Nature Switzerland AG
First Published:First published in Lecture Notes in Computer Science 12721: 293-297
Publisher Policy:Reproduced in accordance with the publisher copyright policy
Related URLs:

University Staff: Request a correction | Enlighten Editors: Update this record