Comfort level classification during patients transport
| Autor: | Aleksandar Peulic, Zeljko Jovanovic, Marina Milosevic, Dragan Jankovic |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Male
Computer science 02 engineering and technology computer.software_genre Accelerometer Severity of Illness Index 0302 clinical medicine Android Patient Transport Android (operating system) Patient Comfort Child Patient comfort Age Factors Middle Aged Mobile Applications Transportation of Patients Child Preschool Android application Female Smartphone Information Systems Adult Adolescent education 0206 medical engineering Biomedical Engineering Biophysics Health Informatics Bioengineering Machine learning Biomaterials Multiclass classification 03 medical and health sciences Naive Bayes classifier Young Adult Sex Factors comfort Humans patients transport business.industry Infant Newborn Infant Bayes Theorem multi-class classification 020601 biomedical engineering Support vector machine Artificial intelligence business computer 030217 neurology & neurosurgery |
| Zdroj: | Technology and Health Care |
| ISSN: | 1878-7401 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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