TY  - JOUR
AU  - Jovanović, Željko
AU  - Milošević, Marina
AU  - Janković, Dragan
AU  - Peulić, Aleksandar
PY  - 2019
UR  - https://gery.gef.bg.ac.rs/handle/123456789/970
AB  - BACKGROUND: Passenger comfort is affected by many factors. Patient comfort is even more specific due to its mental and physical health condition. OBJECTIVE: Developing a system for monitoring patient transport conditions with the comfort level classification, which is affected by the patient parameters. METHODS: Smartphone with the developed Android application was installed in an EMS to monitor patient transport between medical institutions. As a result, 10 calculated parameters are generated in addition to the GPS data and the subjective comfort level. Three classifiers are used to classify the transportation. At the end, the adjustment of classified comfort levels is performed based on the patient's medical condition, age and gender. RESULTS: Modified SVM classifier provided the best overall classification results with the precision of 90.8%. Furthermore, a model that represents patient sensitivity to transport vibration, based on the patient's medical condition, is proposed and the final classification results are presented. CONCLUSIONS: The Android application is mobile, simple to install and use. According to the obtained results, SVM and Naive Bayes classifier gave satisfying results while KNN should be avoided. The developed model takes transport comfort and the patient's medical condition into consideration, so it is suitable for the patient transport comfort classification.
PB  - IOS Press, Amsterdam
T2  - Technology and Health Care
T1  - Comfort level classification during patients transport
VL  - 27
IS  - 1
SP  - 61
EP  - 77
DO  - 10.3233/THC-181411
UR  - https://hdl.handle.net/21.15107/rcub_gery_970
ER  - 

@article{
author = "Jovanović, Željko and Milošević, Marina and Janković, Dragan and Peulić, Aleksandar",
year = "2019",
abstract = "BACKGROUND: Passenger comfort is affected by many factors. Patient comfort is even more specific due to its mental and physical health condition. OBJECTIVE: Developing a system for monitoring patient transport conditions with the comfort level classification, which is affected by the patient parameters. METHODS: Smartphone with the developed Android application was installed in an EMS to monitor patient transport between medical institutions. As a result, 10 calculated parameters are generated in addition to the GPS data and the subjective comfort level. Three classifiers are used to classify the transportation. At the end, the adjustment of classified comfort levels is performed based on the patient's medical condition, age and gender. RESULTS: Modified SVM classifier provided the best overall classification results with the precision of 90.8%. Furthermore, a model that represents patient sensitivity to transport vibration, based on the patient's medical condition, is proposed and the final classification results are presented. CONCLUSIONS: The Android application is mobile, simple to install and use. According to the obtained results, SVM and Naive Bayes classifier gave satisfying results while KNN should be avoided. The developed model takes transport comfort and the patient's medical condition into consideration, so it is suitable for the patient transport comfort classification.",
publisher = "IOS Press, Amsterdam",
journal = "Technology and Health Care",
title = "Comfort level classification during patients transport",
volume = "27",
number = "1",
pages = "61-77",
doi = "10.3233/THC-181411",
url = "https://hdl.handle.net/21.15107/rcub_gery_970"
}

Jovanović, Ž., Milošević, M., Janković, D.,& Peulić, A.. (2019). Comfort level classification during patients transport. in Technology and Health Care
IOS Press, Amsterdam., 27(1), 61-77.
https://doi.org/10.3233/THC-181411
https://hdl.handle.net/21.15107/rcub_gery_970

Jovanović Ž, Milošević M, Janković D, Peulić A. Comfort level classification during patients transport. in Technology and Health Care. 2019;27(1):61-77.
doi:10.3233/THC-181411
https://hdl.handle.net/21.15107/rcub_gery_970 .

Jovanović, Željko, Milošević, Marina, Janković, Dragan, Peulić, Aleksandar, "Comfort level classification during patients transport" in Technology and Health Care, 27, no. 1 (2019):61-77,
https://doi.org/10.3233/THC-181411 .,
https://hdl.handle.net/21.15107/rcub_gery_970 .

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 .