Deep phenotyping of patient lived experience in functional bowel disorders using machine learning.

ABSTRACT: Contemporary clinical management relies on a diagnostic label as the primary guide to treatment. However, individual patients' lived experiences vary more widely than standard diagnostic categories reflect. This is especially true for functional bowel disorders (FBDs), a heterogeneous and challenging group of syndromes where no definitive diagnostic tests, clinical biomarkers, or universally effective treatments exist. Characterising the link between disease and lived experience - in the face of marked patient heterogeneity - requires deep phenotyping of the interactions between multiple characteristics, plausibly achievable only with complex modelling approaches. In a large patient cohort (n = 1175), we developed a machine learning and Bayesian generative graph framework to better underst

SUBMITTER: Ruffle JK 

PROVIDER: S-EPMC12511317 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature