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Evidence-based clinical engineering : machine learning algorithms for prediction of defibrillator performance
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Badnjević, Almir, Gurbeta Pokvić, Lejla, Hasičić, Mehrija, Bandić, Lejla, Mašetić, Zerina, Kovačević, Živorad, Kevrić, Jasmin and Pecchia, Leandro (2019) Evidence-based clinical engineering : machine learning algorithms for prediction of defibrillator performance. Biomedical Signal Processing and Control, 54 . 101629. doi:10.1016/j.bspc.2019.101629
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Official URL: https://doi.org/10.1016/j.bspc.2019.101629
Abstract
Poorly regulated and insufficiently supervised medical devices (MDs) carry high risk of performance accuracy and safety deviations effecting the clinical accuracy and efficiency of patient diagnosis and treatments. Even with the increase of technological sophistication of devices, incidents involving defibrillator malfunction are unfortunately not rare.
To address this, we have developed an automated system based on machine learning algorithms that can predict performance of defibrillators and possible performance failures of the device which can affect performance. To develop an automated system, with high accuracy, overall dataset containing safety and performance measurements data was acquired from periodical safety and performance inspections of 1221 defibrillator. These inspections were carried out in period 2015–2017 in private and public healthcare institutions in Bosnia and Herzegovina by ISO 17,020 accredited laboratory. Out of overall number of samples, 974 of them were used during system development and 247 samples were used for subsequent validation of system performance. During system development, 5 different machine learning algorithms were used, and resulting systems were compared by obtained performance.
| Item Type: | Journal Article | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Divisions: | Faculty of Science > Engineering | ||||||||
| SWORD Depositor: | Library Publications Router | ||||||||
| Journal or Publication Title: | Biomedical Signal Processing and Control | ||||||||
| Publisher: | Elsevier | ||||||||
| ISSN: | 1746-8094 | ||||||||
| Official Date: | September 2019 | ||||||||
| Dates: |
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| Date of first compliant deposit: | 8 October 2019 | ||||||||
| Volume: | 54 | ||||||||
| Article Number: | 101629 | ||||||||
| DOI: | 10.1016/j.bspc.2019.101629 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Access rights to Published version: | Restricted or Subscription Access |
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