Predicting Functional Recovery of Stroke Patients using Age Dependent Model

Model Jeremic, Milan Savic, Ljubica Nikcevic, Dejan Nikolic, Natasa Kovacevic-Kostic

Abstract

Predicting functional recovery of stroke patients is important from both clinical and academic points of view. From the clinical point of view it is important to patients, families and clinical workers. Most importantly, an accurate prediction enables us to provide more accurate prognoses, set goals, manage therapies and improve management of healthcare resources through optimal discharge procedures. For example, being able to predict recovery of particular limbs we could potentially improve advanced planning of safe transfer in an optimally determined time frame. Functional recovery is usually evaluated using various functional indices that evaluate patients’ ability to perform daily living tasks. In this paper we propose to predict functional recovery using two well established functional indices: functional independence measure and Barthels index. We model those indices as a age dependent polynomial functions with unknown coefficients and estimate the unknown parameters. In order to demonstrate applicability of the propose technique we compare the performance of our non-linear polynomial model with the performance on linear MANOVA model.

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Paper Citation


in Harvard Style

Jeremic M., Savic M., Nikcevic L., Nikolic D. and Kovacevic-Kostic N. (2019). Predicting Functional Recovery of Stroke Patients using Age Dependent Model.In Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, ISBN 978-989-758-353-7, pages 241-245. DOI: 10.5220/0007577702410245


in Bibtex Style

@conference{biosignals19,
author={Model Jeremic and Milan Savic and Ljubica Nikcevic and Dejan Nikolic and Natasa Kovacevic-Kostic},
title={Predicting Functional Recovery of Stroke Patients using Age Dependent Model},
booktitle={Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS,},
year={2019},
pages={241-245},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007577702410245},
isbn={978-989-758-353-7},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS,
TI - Predicting Functional Recovery of Stroke Patients using Age Dependent Model
SN - 978-989-758-353-7
AU - Jeremic M.
AU - Savic M.
AU - Nikcevic L.
AU - Nikolic D.
AU - Kovacevic-Kostic N.
PY - 2019
SP - 241
EP - 245
DO - 10.5220/0007577702410245