Phenotyping, genotyping, and prediction of abdominal pain in children using machine learning

BackgroundThe exact mechanisms underlying paediatric abdominal pain (AP) remain unclear due to patient heterogeneity. This study aimed to identify AP phenotypes and develop predictive models to explore associated factors. MethodsIn 13,790 children from a large birth cohort, data on paediatric and maternal demographics and comorbidities were extracted from general practitioner records. Machine learning (ML) clustering was used to identify distinct AP phenotypes, and an ML-based predictive model was developed using demographics and clinical features. Results1,274 children experienced AP (9.2 %) (average age: 8.4 {+/-} 1.1 years, male/female: 615/659), who clustered into three distinct phenotypes: Phenotype 1 with an allergic predisposition (n = 137), Phenotype 2 with maternal comorbidities (n = 676), and Phenotype 3 with minimal other comorbidities (n = 340). As the number of allergic diseases or maternal comorbidities increased, so did the frequency of AP, with 17.6% of children with [&ge;] 3 allergic diseases and 25.6% of children with [&ge;] 3 maternal comorbidities. The predictive model demonstrated moderate performance in predicting paediatric AP (AUC 0.67), showing that a childs ethnicity, paediatric allergic diseases, and maternal comorbidities were key predictive factors. When stratified by ML-predicted probability, observed AP rates were 18.9% in the <40% group, 44.8% in the 40-50% group, 60.6% in the 50-60% group, and 100.0% in the >60% group. ConclusionsOur findings reveal distinct phenotypes and associated factors of paediatric AP by an ML approach. These insights suggest potential targets for future research to clarify the underlying mechanisms of paediatric AP.