Karapintzou, E;
Tsakanikas, V;
Kikidis, D;
Nikitas, C;
Nairn, B;
Pavlou, M;
Bamiou, DE;
... Fotiadis, DI; + view all
(2025)
AI-Enhanced Tele-Rehabilitation: Predictive Modeling for Fall Risk and Treatment Efficacy in Balance Disorders.
In:
2024 IEEE 24th International Conference on Bioinformatics and Bioengineering, BIBE 2024.
(pp. pp. 1-8).
IEEE: Kragujevac, Serbia.
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Abstract
This study focuses on the development of artificial intelligence models to enhance telerehabilitation practices. We utilized diverse datasets to create clinically relevant models for predicting two critical outcomes: fall risk and treatment effectiveness. By applying various machine learning techniques, including K-Nearest Neighbors, Random Forest, Decision Tree, Support Vector Machine, and XGBoost, our models demonstrated high accuracy, sensitivity, and specificity. Notably, the Random Forest model achieved an accuracy of 0.97 in predicting fall risk and 0.96 in assessing treatment effectiveness. These models equip clinicians with powerful tools for data-driven decision-making, ultimately improving patient outcomes in rehabilitation settings.
| Type: | Proceedings paper |
|---|---|
| Title: | AI-Enhanced Tele-Rehabilitation: Predictive Modeling for Fall Risk and Treatment Efficacy in Balance Disorders |
| Event: | BIBE 2024 |
| Dates: | 27 Nov 2024 - 29 Nov 2024 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/BIBE63649.2024.10820488 |
| Publisher version: | https://doi.org/10.1109/bibe63649.2024.10820488 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Support vector machines , Accuracy , Biological system modeling , Decision making , Patient rehabilitation , Predictive models , Sensitivity and specificity , Feature extraction , Robustness , Random forests |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > The Ear Institute |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10208313 |
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