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Heart Rate and Body Temperature Relationship in Children Admitted to PICU: A Machine Learning Approach
Insights
Heart rate (HR) and body temperature (BT) in critically ill children show complex, non-linear relationships with age. Machine learning models better predict HR than linear models, offering improved clinical insights for pediatric intensive care units (PICUs).
Area of Science:
- Pediatric critical care medicine
- Biomedical informatics
- Clinical physiology
Background:
- Vital signs like body temperature (BT) and heart rate (HR) are critical in pediatric intensive care units (PICUs).
- Limited research exists on the HR-BT relationship in critically ill children.
- Understanding these dynamics is vital for accurate patient assessment.
Purpose of the Study:
- To investigate the relationship between heart rate (HR) and body temperature (BT) in pediatric patients (0-18 years) admitted to a PICU.
- To apply machine learning (ML) techniques to model these complex associations.
- To compare ML model performance against traditional linear assumptions.
Main Methods:
- Utilized Gradient Boosting Machines (GBM) with Quantile Regression (QR) for modeling HR, BT, and age.
- Employed hyperparameter tuning to optimize model performance.
- Analyzed data from 4006 pediatric patients admitted to CHU Sainte-Justine (CHUSJ) Hospital.
Main Results:
- Observed decreasing HR with increasing age and increasing HR with higher BT ranges.
- Linear models inaccurately estimated HR, particularly in younger children across different BT ranges.
- The GBM model provided improved accuracy for HR prediction, considering age and BT percentiles.
Conclusions:
- The relationship between HR, BT, and age in critically ill children is non-linear.
- Machine learning offers a more accurate approach to modeling these vital sign dynamics.
- Findings challenge traditional linear assumptions and inform clinical decision-making in PICUs.
Abstract:
Vital signs are crucial clinical measures, with body temperature (BT) and heart rate (HR) being particularly significant. While their association has been studied in adults and children, research in Pediatric Intensive Care Unit (PICU) settings remains limited despite the critical conditions of these patients.
Objective:
This study examines the relationship between HR and BT in children aged 0 to 18 admitted to the PICU at CHU Sainte-Justine (CHUSJ) Hospital.
Methods:
Machine learning (ML) techniques, including Gradient Boosting Machines (GBM) with Quantile Regression (QR), were applied to capture the relationship between HR, BT, and age, optimizing model performance through hyperparameter tuning.
Results:
Analyzing data from 4006 children, we observed a consistent trend of decreasing HR with increasing age and rising HR with higher BT ranges. Linear models often underestimated HR at lower BT ranges and overestimated it at higher ranges, especially in younger age groups. The GBM model demonstrated improved accuracy and supported a user-friendly interface for HR predictions based on BT, age, and HR percentiles. Qualitative observations indicated that linear models underestimated HR at lower BT ranges and overestimated it at higher ones, particularly in younger children. These findings challenge the direct linear association assumed in prior studies.
Conclusion:
This study provides new insights into the non-linear dynamics between HR, BT, and age in critically ill children, emphasizing further research to quantify and understand these relationships.
Significance:
By refining predictive models and re-evaluating traditional assumptions, this work provides valuable insights for improving clinical decision-making in PICU settings.
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