ArticleIEEE transactions on bio-medical engineering2017
Is the Sequence of SuperAlarm Triggers More Predictive Than Sequence of the Currently Utilized Patient Monitor Alarms?
Article in IEEE transactions on bio-medical engineering, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Developing a Scalable Annotation Method for Large Datasets That Enhances Alarms With Actionability Data to Increase Informativeness: Mixed Methods Approach.Journal of medical Internet research · 2025Article
- Generalizability of SuperAlarm via Cross-Institutional Performance Evaluation.IEEE access : practical innovations, open solutions · 2020Article
- An algorithm strategy for precise patient monitoring in a connected healthcare enterprise.NPJ digital medicine · 2019Review
- Predict In-Hospital Code Blue Events using Monitor Alarms through Deep Learning Approach.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2018Article
- Patient characteristics associated with false arrhythmia alarms in intensive care.Therapeutics and clinical risk management · 2017Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
objectiveOur previous studies have shown that "code blue" events can be predicted by SuperAlarm patterns that are multivariate combinations of monitor alarms and laboratory test results cooccurring frequently preceding the events but rarely among control patients. Deploying these patterns to the monitor data streams can generate SuperAlarm sequences. The objective of this study is to test the hypothesis that SuperAlarm sequences may contain more predictive sequential patterns than monitor alarms sequences.
methodsMonitor alarms and laboratory test results are extracted from a total of 254 adult coded and 2213 control patients. The training dataset is composed of subsequences that are sampled from complete sequences and then further represented as fixed-dimensional vectors by the term frequency inverse document frequency method. The information gain technique and weighted support vector machine are adopted to select the most relevant features and train a classifier to differentiate sequences between coded patients and control patients. Performances are assessed based on an independent dataset using three metrics: sensitivity of lead time (Sen
resultsThe performance of 12-h-long sequences of SuperAlarm can yield a Sen CONCLUSION AND SIGNIFICANCE: The results demonstrate that SuperAlarm sequences indeed outperform monitor alarm sequences and suggest that one can focus on sequential patterns from SuperAlarm sequences to develop more precise patient monitoring solutions.
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Registered trials
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