Using Supervised Machine Learning to Classify Real Alerts and Artifact in Online Multi-signal Vital Sign Monitoring Data

OBJECTIVE: Use machine-learning (ML) algorithms to classify alerts as real or artifacts in online noninvasive vital sign (VS) data streams to reduce alarm fatigue and missed true instability. METHODS: Using a 24-bed trauma step-down unit’s non-invasive VS monitoring data (heart rate [HR], respirator...

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Bibliografiset tiedot
Julkaisussa:Crit Care Med
Päätekijät: Chen, Lujie, Dubrawski, Artur, Wang, Donghan, Fiterau, Madalina, Guillame-Bert, Mathieu, Bose, Eliezer, Kaynar, Ata M., Wallace, David J., Guttendorf, Jane, Clermont, Gilles, Pinsky, Michael R., Hravnak, Marilyn
Aineistotyyppi: Artigo
Kieli:Inglês
Julkaistu: 2016
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Article
Linkit:https://ncbi.nlm.nih.gov/pmc/articles/PMC4911247/
https://ncbi.nlm.nih.gov/pubmed/26992068
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1097/CCM.0000000000001660
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