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

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 understand the lived experience of FBDs. Iterating through 59 factors available from routine clinical care, spanning patient demography, diagnosis, symptomatology, life impact, mental health indices, healthcare access requirements, COVID-19 impact, and treatment effectiveness, machine models were used to quantify the predictive fidelity of one feature from the remainder. Bayesian stochastic block models were used to delineate the network community structure underpinning the heterogeneous lived experience of FBDs. Machine models quantified patient personal health rating (R