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Lookup NU author(s): Cho Ng, Trevor Liddle, Professor Yan Yiannakou
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2025. The Author(s). 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 (R2 0.35), anxiety and depression severity (R2 0.54), employment status (balanced accuracy 96%), frequency of healthcare attendance (R2 0.71), and patient-reported treatment effectiveness variably (R2 range 0.08-0.41). Contrary to the view of many healthcare professionals, the greatest model predictors of patient-reported health and quality of life were life impact, mental well-being, employment status, and age, rather than diagnostic group or symptom severity. Patients responsive to one treatment were more likely to respond to another, leaving many others refractory to all. Clinical assessment of patients with FBDs should be less concerned with diagnostic classification than with the wider life impact of illness, including mental health and employment. The stratification of treatment response (and resistance) has implications for clinical practice and trial design, necessitating further research.
Author(s): Ruffle JK, Henderson M, Ng CE, Liddle T, Nelson APK, Nachev P, Knowles CH, Yiannakou Y
Publication type: Article
Publication status: Published
Journal: Scientific Reports
Year: 2025
Volume: 15
Online publication date: 09/10/2025
Acceptance date: 08/09/2025
Date deposited: 20/10/2025
ISSN (electronic): 2045-2322
Publisher: Springer Nature
URL: https://doi.org/10.1038/s41598-025-19273-3
DOI: 10.1038/s41598-025-19273-3
Data Access Statement: Trained model weights are available upon request from the corresponding author. Data and code availability align with UK government policy on open-source code. Patient data are not available for dissemination under the ethical framework that governs its use. Patient anonymized code excerpts are available at https://github.com/jamesruffle/perspective-ai. Analyses were predominantly performed within a Python (version 3.6.9) environment with the following software packages: graph-tool, imblearn, Matplotlib, NumPy, pandas, SciPy, Scikit-learn, seaborn, SHAP and XGBoost. MICE was performed in R (version 4.1.3).
PubMed id: 41068191
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