Abstract
Heart failure poses a significant global health burden with high prevalence and mortality rates. A promising possibility in this context is the constant monitoring of the patients through telemedicine. The aim of this work is to present a digital twin of a patient at risk of heart failure. Applying machine learning to the recorded data of the patient, the system is able to early detect potential issues and improve the outcome.
| Original language | English |
|---|---|
| Title of host publication | Digital Health and Informatics Innovations for Sustainable Health Care Systems - Proceedings of MIE 2024 |
| Editors | John Mantas, Arie Hasman, George Demiris, Kaija Saranto, Michael Marschollek, Theodoros N. Arvanitis, Ivana Ognjanovic, Arriel Benis, Parisis Gallos, Emmanouil Zoulias, Elisavet Andrikopoulou |
| Number of pages | 2 |
| Publisher | IOS Press |
| Publication date | 22 Aug 2024 |
| Pages | 875-876 |
| ISBN (Electronic) | 9781643685335 |
| DOIs | |
| Publication status | Published - 22 Aug 2024 |
| Event | 34th Medical Informatics Europe Conference, MIE 2024 - Athens, Greece Duration: 25 Aug 2024 → 29 Aug 2024 |
Conference
| Conference | 34th Medical Informatics Europe Conference, MIE 2024 |
|---|---|
| Country/Territory | Greece |
| City | Athens |
| Period | 25/08/2024 → 29/08/2024 |
| Sponsor | Dedalus, Elsevier, JMIR, Trust-IT |
| Series | Studies in Health Technology and Informatics |
|---|---|
| Volume | 316 |
| ISSN | 0926-9630 |
Bibliographical note
Publisher Copyright:© 2024 The Authors.
Keywords
- Digital Twin
- heart failure
- personalized medicine
- telemedicine