  Vital signs have been essential clinical measures. Among these, body
temperature (BT) and heart rate (HR) are particularly significant, and numerous
studies explored their association in hospitalized adults and children.
However, a lack of in-depth research persists in children admitted to the
pediatric intensive care unit (PICU) despite their critical condition requiring
particular attention. Objective: In this study, we explore the relationship
between HR and BT in children from 0 to 18 years old admitted to the PICU of
CHU Sainte-Justine Hospital. Methods: We applied Machine learning (ML)
techniques to unravel subtle patterns and dependencies within our dataset to
achieve this objective. Each algorithm undergoes meticulous hyperparameter
tuning to optimize the model performance. Results: Our findings align with
prior research, revealing a consistent trend of decreasing HR with increasing
patient age, confirming the observed inverse correlation. Furthermore, a
thorough analysis identifies Gradient Boosting Machines (GBM) implemented with
Quantile regression (QR), as the most fitting model, effectively capturing the
non-linear relationship between HR, BT, and age. Through testing the HR
prediction model based on age and BT, the predictive model between the 5th and
95th percentiles accurately demonstrates the declining trend of HR with age,
while HR increase with BT. Based on that, we have developed a user-friendly
interface tailored to generate HR predictions at different percentiles based on
three key input parameters : current HR, current BT, and patient's age. The
resulting output enables caregivers to quickly determine whether a patient's HR
falls within or outside the normal range, facilitating informed clinical
decision-making. Thus, our results challenge previous studies' presumed direct
linear association between HR and BT.
