@article{Jovanovic201961,
title = {Comfort level classification during patients transport},
author = {Zeljko Jovanovic and Marina Milosevic and Dragan Jankovic and Aleksandar Peulic},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061066421\&doi=10.3233%2fTHC-181411\&partnerID=40\&md5=c8aec01521ed53f05000d8241c0d2c3d},
doi = {10.3233/THC-181411},
year = {2019},
date = {2019-01-01},
journal = {Technology and Health Care},
volume = {27},
number = {1},
pages = {61 \textendash 77},
publisher = {IOS Press},
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. © 2019 - IOS Press and the authors. All rights reserved},
keywords = {Adolescent; Adult; Age Factors; Bayes Theorem; Child; Child, Preschool; Female; Humans; Infant; Infant},
pubstate = {published},
tppubtype = {article}
}
