Machine learning approach to dissect the clinical heterogeneity of IBD-associated fatigue
Bibliographic record
Abstract
Abstract Extreme fatigue is a clinical symptom that affects >50% of individuals with Inflammatory Bowel Disease (IBD), with a similar prevalence across many common immune-mediated inflammatory diseases (IMIDs). Despite its ubiquity, human scientific studies have yet to explain the mechanistic basis of this pervasive and complex symptom. One fundamental reason for this, is our inability to account for the clinical heterogeneity of fatigue with its multifactorial nature. We present the conceptual machine learning (ML) framework to dissect the complex nature of fatigue using one of the largest prospectively captured, real-world patient-reported outcome (PROs) on wellbeing from three contemporaneous cohorts (2020-present), totalling 2,970 responses from 2,290 participants across the UK and internationally, including non-IBD controls with 100 lines of clinical metadata. We systematically defined the (1) threshold of fatigue as our primary outcome (≥10/14 fatigue days) to build our ML approach, (2) utilised routinely available clinical data that can be used at a population-level analysis, (3) employed seven different ML methods with external validation in 3 different cohorts in UK, Spain and Australia (n=252), (4) employed Shapley Additive Explanations (SHAP) analysis to break down the clinical heterogeneity to allow the examination of clinical predictive factors at an individual level; and finally (5), investigate whether there are distinct clusters of fatigue patients. We found that ML models performed comparably (AUC/C-index ∼0.7) on external validation with SHAP analysis showing interpretable, individualised fatigue drivers and five distinct fatigue phenotypes, including a subgroup of young males with significantly lower fatigue burden. Our data therefore provides the ML ‘roadmap’ to predict and deconstruct fatigue in IBD and potentially also more widely in IMIDs, enabling patient-level dissection beyond symptom-based classification with the ability to integrate deep molecular data. This is a step towards future clinical-scientific AI models with the immediate clinical application to stratify patients to human experimental studies to better understand the dominant mechanisms that drive fatigue at an individual level.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".