Artificial intelligence for weight estimation in paediatric emergency care

Abstract

Objective: To develop and validate a paediatric weight estimation model adapted to the characteristics of the Spanish population as an alternative to currently extended methods.

Methods: Anthropometric data in a cohort of 11 287 children were used to develop machine learning models to predict weight using height and the body mass index (BMI) quartile (as surrogate for body habitus (BH)). The models were later validated in an independent cohort of 780 children admitted to paediatric emergencies in two other hospitals. The proportion of patients with a given absolute percent error (APE) was calculated for various APE thresholds and compared with the available weight estimation methods to date. The concordance between the BMI-based BH and the visual assessment was evaluated, and the effect of the visual estimation of the BH was assessed in the performance of the model.

Results: The machine learning model with the highest accuracy was selected as the final algorithm. The model estimates weight from the child's height and BH (under-, normal- and overweight) based on a support vector machine with a Gaussian-kernel (SVM-G). The model presented an APE<10% and <20% for 74.7% and 96.7% of the children, outperforming other available predictive formulas by 3.2-37.5% and 1.3-29.1%, respectively. Low concordance was observed between the theoretical and visually assessed BH in 36.7% of the children, showing larger errors in children under 2 years.

Conclusions: The proposed SVM-G is a valid and safe tool to estimate weight in paediatric emergencies, more accurate than other local and global proposals.

Keywords: Child Health; Machine Learning; Resuscitation.

Figure 1. Performance of the three machine learning models proposed in the study as a function of the proportion of estimates with an absolute percent error (APE) lower than the limit indicated in the horizontal axis. BH, body habitus; LR, simple linear regression; PR, second-order polynomial regression; SVM-G, Gaussian-kernel support vector machine.

Figure 2. Bland-Altman plot showing the percent error (PE) as a function of the actual weights for the Gaussian-kernel support vector machine (SVM-G) model when visual (a) and theoretical (b) body habitus (BH) groups are used for model validation. The central line indicates the mean PE, whereas the exterior lines represent the 95% CI of the mean estimate.

Figure 3. Comparison of the available weight estimation methods and the proposed Gaussian-kernel support vector machine (SVM-G) model in terms of the median absolute percentage error (APE) (IQR) stratified over the three values of body habitus (BH) group: under-, normal- and overweight.

Figure 4. Proportion of errors in the visual estimation of the body habitus (BH) group by nursing staff. The results are shown in terms of age and sex. P-values represent the statistically significant difference in sex within each age interval.

Figure 5. Distribution of the observations based on the height and body mass index (BMI). Black circles show the BMI thresholds used in each height range (ranges of 5 cm) for the assignment of the different body habitus (BH) levels. The white section corresponds to the BMI interval where 75% of the visually estimated BH errors concentrate in each height interval.

Figure 6. An implementation of a colour-based tape according to the Gaussian-kernel support vector machine algorithm.