Madjarov, Gjorgji and Gams, Matjaž and Lustrek, Mitja and Trajkovik, Vladimir and Zdravevski, Eftim and Koceski, Saso and Lameski, Petre (2018) Patient health status prediction and diagnostics based on sensor data and machine learning. [Project] (In Press)
Full text not available from this repository.Abstract
The increasing number of elderly population also increases the healthcare demand and with it the healthcare costs for each country. This is mainly due to the increased need of patient monitoring where highly educated medical personal working hours are spent observing patients’ health parameters obtained by different measuring units. The introduction to different kinds of sensors in the hospitals and the patients’ living environment and the advances in electronics and computing power has allowed generation of large amounts of sensory data for each patient. The available sensory data obtained from both the living environment and from medical institutions could help building predictive models that would notify the doctor before a critical situation has arise. In this project we will focus our research towards design and implementation of Machine learning based predictive and decision support models that would allow semi-automated monitoring of patients and would, based on the available sensor data, ease the diagnostics of the patients and also even predict patients critical states before they even happen which would greatly decrease the doctors response time and increase the patients health and living quality.
| Item Type: | Project |
|---|---|
| Subjects: | Engineering and Technology > Electrical engineering, electronic engineering, information engineering |
| Divisions: | Faculty of Computer Science |
| Depositing User: | Saso Koceski |
| Date Deposited: | 15 Feb 2019 08:47 |
| Last Modified: | 15 Feb 2019 08:47 |
| URI: | https://eprints.ugd.edu.mk/id/eprint/21576 |
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