Publications by authors named "Yonghyuk Jeon"
Article Synopsis
- The study addresses the challenges of assessing sedation levels in critically ill children by developing a deep learning model that uses heart rate variability (HRV) and vital signs.
- Conducted in a pediatric intensive care unit, the researchers analyzed data from 324 patients to predict sedation levels using the Richmond Agitation-Sedation Scale (RASS).
- Results showed excellent model performance, suggesting that combining HRV metrics with vital signs could enable more effective and continuous sedation monitoring in critically ill pediatric patients, although further validation is required.
Early detection of deteriorating patients is important to prevent life-threatening events and improve clinical outcomes. Efforts have been made to detect or prevent major events such as cardiopulmonary resuscitation, but previously developed tools are often complicated and time-consuming, rendering them impractical. To overcome this problem, we designed this study to create a deep learning prediction model that predicts critical events with simplified variables.
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