Evidence map›Paper›PMID 40331554›Full record

ArticleTechnology and health care : official journal of the European Society for Engineering and Medicine2025

Enhancing mechanical ventilator reliability through machine learning based predictive maintenance.

Žarko Peruničić, Ivana Lalatović, Lemana Spahić, Adna Ašić, Lejla Gurbeta Pokvić, Almir Badnjević

Expression of concernAbstract read
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In one paragraph

Article in Technology and health care : official journal of the European Society for Engineering and Medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It carries an expression of concern. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Žarko PeruničićUniversity of Donja Gorica, Podgorica, Montenegro.
Ivana LalatovićUniversity of Donja Gorica, Podgorica, Montenegro.
Lemana SpahićResearch Institute Verlab for Biomedical Engineering, Medical Devices and Artificial Intelligence, Sarajevo, Bosnia and Herzegovina.ORCID 0000-0002-0922-6335
Adna AšićResearch Institute Verlab for Biomedical Engineering, Medical Devices and Artificial Intelligence, Sarajevo, Bosnia and Herzegovina.
Lejla Gurbeta PokvićUniversity of Donja Gorica, Podgorica, Montenegro.
Almir BadnjevićFaculty of Pharmacy, University of Sarajevo, Sarajevo, Bosnia and Herzegovina.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BackgroundWith the advancement of Artificial Intelligence (AI), clinical engineering has witnessed transformative opportunities, enabling predictive maintenance of medical devices, optimization of healthcare workflows, and personalized patient care. Respiratory equipment plays a vital role in modern healthcare, supporting patients with compromised or impaired respiratory capacities. However, ensuring the reliability and safety of these devices is crucial to prevent adverse events and ensure patient well-being.ObjectiveThis study aims to explore machine learning techniques to enhance predictive maintenance for mechanical ventilators.

Indexed as

Machine LearningVentilators, MechanicalAlgorithmsBayes TheoremDecision TreesEquipment FailureHumansReproducibility of ResultsRespiration, ArtificialSupport Vector Machineclinical engineeringmechanical ventilatorsmedical deviceperformance assessmentpost-market surveillance

Identifiers

PMID40331554

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.