ArticleAnnual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference2018
Predict In-Hospital Code Blue Events using Monitor Alarms through Deep Learning Approach.
Article in Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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.
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.
Who cites it
2 citing papers in PubMed, 3 citations in OpenAlex.
- Computational approaches to alleviate alarm fatigue in intensive care medicine: A systematic literature review.Frontiers in digital health · 2022Review
- Electrocardiographic right ventricular strain precedes hypoxic pulseless electrical activity cardiac arrests: Looking beyond pulmonary embolism.Resuscitation · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
Bedside monitors in hospital intensive care units (ICUs) are known to produce excessive false alarms that could desensitize caregivers, resulting in delayed or even missed clinical interventions to life-threatening events. Our previous studies proposed a framework aggregating information in monitor alarm data by mining frequent alarm combinations (i.e., SuperAlarm) that are predictive to clinical endpoints, such as code blue events, in an effort to address this critical issue. In the present pilot study, we hypothesize that sequential deep learning models, specifically long-short term memory (LSTM), could capture time-depend features in continuous alarm sequences preceding code blue events and these features may be predictive of these endpoints. LSTM models are trained from continuous alarm sequences in various window lengths preceding code blue events, and the preliminary results showed the best performance reached an AUC of 0.8549. With the selection of optimal cutoff threshold, the 2-hour window model achieved 85.75% sensitivity and 72.61% specificity, respectively.
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Registered trials
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.