Patient comfort level prediction during transport using artificial neural network
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 / accelerometerSource:
Turkish Journal of Electrical Engineering and Computer Sciences, 2019, 27, 4, 2817-2832Publisher:
- 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
Collections
Institution/Community
Geografski fakultetTY - 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 .