Machine learning-based phenogroups and prediction model in patients with functional gastrointestinal disorders to reveal distinct disease subsets associated with gas production

Symptom-based subtyping for functional gastrointestinal disorders (FGIDs) has limited value in identifying underlying mechanisms and guiding therapeutic strategies. Small intestinal dysbiosis is implicated in the development of FGIDs. We tested if machine learning (ML) algorithms utilizing both gastrointestinal (GI) symptom characteristics and lactulose breath tests could provide distinct clusters.