Name
Abstract | ||
|---|---|---|
Tinnitus is the perception of a phantom sound and the individual's reaction to it. Although much progress has been made, tinnitus remains an unresolved scientific and clinical issue, affecting more than 10% of the general population and having a high prevalence and socioeconomic burden. Clinical decision support systems (CDSS) are used to assist clinicians in their complex decision-making processes, having been proved that they improve healthcare delivery. In this paper, we present a CDSS for tinnitus, attempting to address the question which treatment approach is optimal for a particular patient based on specific parameters. The CDSS will be developed in the context of the EU-funded "UNITI" project and, after the project completion, it will be able to determine the suitability and expected attachment of a particular patient to a list of available clinical interventions, utilizing predictive and classification machine learning models. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/EMBC46164.2021.9630137 | 2021 43RD ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY (EMBC) |
DocType | Volume | ISSN |
Conference | 2021 | 1557-170X |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michail Sarafidis | 1 | 0 | 0.34 |
| Ourania Manta | 2 | 0 | 0.34 |
| Ioannis Kouris | 3 | 0 | 0.34 |
| Winfried Schlee | 4 | 0 | 0.68 |
| Dimitrios Kikidis | 5 | 0 | 0.34 |
| Eleftheria Vellidou | 6 | 0 | 0.34 |
| Dimitrios Koutsouris | 7 | 0 | 0.34 |