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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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| 出版年: | Crit Care Med |
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| 主要な著者: | , , , , , , , , , , , |
| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
2016
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| 主題: | |
| オンライン･アクセス: | 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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