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Patient comfort level prediction during transport using artificial neural network

Authorized Users Only
2019
Authors
Jovanović, Željko
Blagojević, Maria
Janković, Dragan
Peulić, Aleksandar
Article (Published version)
Metadata
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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.
Keywords:
Patient comfort / artificial neural network / android application / accelerometer
Source:
Turkish Journal of Electrical Engineering and Computer Sciences, 2019, 27, 4, 2817-2832
Publisher:
  • Tubitak Scientific & Technical Research Council Turkey, Ankara
Funding / projects:
  • Development and Modeling of Energy-Efficient, Adaptable, Multiprocessor and Multisensor Low-Power Electronic Systems (RS-32043)
  • Development of new information and communication technologies, based on advanced mathematical methods, with applications in medicine, telecommunications, power systems, protection of national heritage and education (RS-44006)

DOI: 10.3906/elk-1807-258

ISSN: 1300-0632

WoS: 000482742800032

Scopus: 2-s2.0-85072603479
[ Google Scholar ]
3
2
URI
https://gery.gef.bg.ac.rs/handle/123456789/969
Collections
  • Radovi istraživača
Institution/Community
Geografski fakultet
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  - conv_1533
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 = "conv_1533"
}
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
conv_1533
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
conv_1533 .
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 .,
conv_1533 .

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