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An unsupervised feature learning approach to reduce false alarm rate in ICUs
The high rate of false alarms in intensive care units (ICUs) is one of the top challenges of using medical technology in hospitals. These false alarms are often caused by patients’ movements, detachment of monitoring sensors, or different sources of noise and interference that impact the collected s...
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| Pubblicato in: | Annu Int Conf IEEE Eng Med Biol Soc |
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| Autori principali: | , , , , , |
| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
2019
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| Soggetti: | |
| Accesso online: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7552437/ https://ncbi.nlm.nih.gov/pubmed/31945913 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1109/EMBC.2019.8857034 |
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