TY  - JOUR
AU  - Jovanović, Željko
AU  - Blagojević, Maria
AU  - Janković, Dragan
AU  - Peulić, Aleksandar
PY  - 2019
UR  - https://gery.gef.bg.ac.rs/handle/123456789/969
AB  - Since patient comfort during transport is a matter of paramount importance, this paper aims to determine the possibilities of applying neural networks for its prediction and monitoring. Specific objectives of the research include monitoring and predicting patient transport comfort, with subjective assessment of comfort by medical personnel. An original Android application that collects signals from an accelerometer and a GPS sensor was used with the aim of achieving the research goals. The collected signals were processed and a total of twelve parameters were calculated. A multilayer perceptron was created in the proposed research. The evaluation results indicate acceptable accuracy and give the possibility to apply the same model to the next patient transport. The root mean square error was 0.0215 and the overall confusion matrix prediction accuracy was 90.07%. Moreover, the results were validated in real usage. The limitations and future work are highlighted.
PB  - Tubitak Scientific & Technical Research Council Turkey, Ankara
T2  - Turkish Journal of Electrical Engineering and Computer Sciences
T1  - Patient comfort level prediction during transport using artificial neural network
VL  - 27
IS  - 4
SP  - 2817
EP  - 2832
DO  - 10.3906/elk-1807-258
UR  - https://hdl.handle.net/21.15107/rcub_gery_969
ER  - 

@article{
author = "Jovanović, Željko and Blagojević, Maria and Janković, Dragan and Peulić, Aleksandar",
year = "2019",
abstract = "Since patient comfort during transport is a matter of paramount importance, this paper aims to determine the possibilities of applying neural networks for its prediction and monitoring. Specific objectives of the research include monitoring and predicting patient transport comfort, with subjective assessment of comfort by medical personnel. An original Android application that collects signals from an accelerometer and a GPS sensor was used with the aim of achieving the research goals. The collected signals were processed and a total of twelve parameters were calculated. A multilayer perceptron was created in the proposed research. The evaluation results indicate acceptable accuracy and give the possibility to apply the same model to the next patient transport. The root mean square error was 0.0215 and the overall confusion matrix prediction accuracy was 90.07%. Moreover, the results were validated in real usage. The limitations and future work are highlighted.",
publisher = "Tubitak Scientific & Technical Research Council Turkey, Ankara",
journal = "Turkish Journal of Electrical Engineering and Computer Sciences",
title = "Patient comfort level prediction during transport using artificial neural network",
volume = "27",
number = "4",
pages = "2817-2832",
doi = "10.3906/elk-1807-258",
url = "https://hdl.handle.net/21.15107/rcub_gery_969"
}

Jovanović, Ž., Blagojević, M., Janković, D.,& Peulić, A.. (2019). Patient comfort level prediction during transport using artificial neural network. in Turkish Journal of Electrical Engineering and Computer Sciences
Tubitak Scientific & Technical Research Council Turkey, Ankara., 27(4), 2817-2832.
https://doi.org/10.3906/elk-1807-258
https://hdl.handle.net/21.15107/rcub_gery_969

Jovanović Ž, Blagojević M, Janković D, Peulić A. Patient comfort level prediction during transport using artificial neural network. in Turkish Journal of Electrical Engineering and Computer Sciences. 2019;27(4):2817-2832.
doi:10.3906/elk-1807-258
https://hdl.handle.net/21.15107/rcub_gery_969 .

Jovanović, Željko, Blagojević, Maria, Janković, Dragan, Peulić, Aleksandar, "Patient comfort level prediction during transport using artificial neural network" in Turkish Journal of Electrical Engineering and Computer Sciences, 27, no. 4 (2019):2817-2832,
https://doi.org/10.3906/elk-1807-258 .,
https://hdl.handle.net/21.15107/rcub_gery_969 .