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Background Early diagnosis is essential for effective stroke therapy. Strokes in hospitalized patients are associated with worse outcomes compared with strokes in the community. We derived and validated an algorithm to identify strokes by monitoring upper limb movements in hospitalized patients. Methods and Results A prospective case–control study in hospitalized patients evaluated bilateral arm accelerometry from patients with acute stroke with lateralized weakness and controls without stroke. We derived a stroke classifier algorithm from 123 controls and 77 acute stroke cases and then validated the performance in a separate cohort of 167 controls and 33 acute strokes, measuring false alarm rates in nonstroke controls and time to detection in stroke cases. Faster detection time was associated with more false alarms. With a median false alarm rate among nonstroke controls of 3.6 (interquartile range [IQR], 2.1–5.0) alarms per patient per day, the median time to detection was 15.0 (IQR, 8.0–73.5) minutes. A median false alarm rate of 1.1 (IQR. 0–2.2) per patient per day was associated with a median time to stroke detection of 29.0 (IQR, 11.0–58.0) minutes. There were no differences in algorithm performance for subgroups dichotomized by age, sex, race, handedness, nondominant hemisphere involvement, intensive care unit versus ward, or daytime versus nighttime. Conclusions Arm movement data can be used to detect asymmetry indicative of stroke in hospitalized patients with a low false alarm rate. Additional studies are needed to demonstrate clinical usefulness.
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A Feature Selection Method Based on Shapley Value to False Alarm Reduction in ICUs A Genetic-Algorithm Approach
High false alarm rate in intensive care units (ICUs) has been identified as one of the most critical medical challenges in recent years. This often results in overwhelming the clinical staff by numerous false or unurgent alarms and decreasing the quality of care through enhancing the probability of missing true alarms as well as causing delirium, stress, sleep deprivation and depressed immune systems for patients. One major cause of false alarms in clinical practice is that the collected signals from different devices are processed individually to trigger an alarm, while there exists a considerable chance that the signal collected from one device is corrupted by noise or motion artifacts. In this paper, we propose a low-computational complexity yet accurate game-theoretic feature selection method which is based on a genetic algorithm that identifies the most informative biomarkers across the signals collected from various monitoring devices and can considerably reduce the rate of false alarms.
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- Award ID(s):
- 1657260
- PAR ID:
- 10053371
- Date Published:
- Journal Name:
- A Feature Selection Method Based on Shapley Value to False Alarm Reduction in ICUs A Genetic-Algorithm Approach
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/EMBC.2018.8512266
