SynthesisJournal of Crohn's & colitis2022
Machine Learning-based Prediction Models for Diagnosis and Prognosis in Inflammatory Bowel Diseases: A Systematic Review.
Synthesis in Journal of Crohn's & colitis, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
36 citing papers in PubMed, 2 syntheses or guidelines pooled it, 48 citations in OpenAlex.
- The predictive value of machine learning for the response to anti-TNF-α therapy in patients with Crohn's disease: a systematic review and meta-analysis.Frontiers in immunology · 2026Pooled it
- A Systematic Review of Artificial Intelligence and Machine Learning Applications to Inflammatory Bowel Disease, with Practical Guidelines for Interpretation.Inflammatory bowel diseases · 2022Pooled it
- Review
- Machine Learning-Based Prediction of Treatment Response to Vitamin DBiomedicines · 2026Article
- Multidisciplinary prediction of running-related injuries using machine learning.NPJ digital medicine · 2026Article
- Decoding host-microbiome interactions through integrative multi-omics and artificial intelligence: from biological mechanisms to predictive modelling.Frontiers in microbiomes · 2026Review
- Gradient Boosting Machine-Based Prognostic Model with SurvSHAP Analysis for Predicting Secondary Loss of Response to Infliximab Therapy in Crohn's Disease.Drug design, development and therapy · 2026Article
- Language-informed treatment recommendations in psychiatry: bridging the gap in individual predictability.Frontiers in psychiatry · 2026Article
- Explainable random forest-SHAP framework for Montreal phenotype-stratified prediction of one-year complications in Crohn's disease.Frontiers in physiology · 2026Article
- Performance of Novel Inflammatory Bowel Disease-Specific Models for Risk Adjustment.Risk management and healthcare policy · 2026Article
- Machine Learning-Based Prediction Model Construction for Type 2 Diabetes Mellitus: A Comparison of Algorithms and Multilevel Risk Factor Analysis.Journal of diabetes research · 2026Article
- Identifying key clinical and biochemical predictors of treatment outcomes in inflammatory bowel disease: a real-world evidence study.Scientific reports · 2025Article
- Federated learning for early severity prediction in acute pancreatitis: a multi-center study.BMC gastroenterology · 2025Article
- Artificial Intelligence in Advancing Inflammatory Bowel Disease Management: Setting New Standards.Cancers · 2025Review
- Prediction of Extraintestinal Manifestations in Inflammatory Bowel Disease Using Clinical and Genetic Variables with Machine Learning in a Latin IBD Group.International journal of molecular sciences · 2025Article
- Development and validation of a risk prediction tool for the diagnosis of inflammatory bowel disease in patients presenting in primary care with abdominal symptoms.Journal of Crohn's & colitis · 2025Article
- Machine learning-based prediction of response to Ustekinumab with Crohn's disease.Therapeutic advances in gastroenterology · 2025Article
- Development and validation of a novel model based on clinical characteristics to predict natural disease course progression in patients with stricturing Crohn's disease.Therapeutic advances in gastroenterology · 2025Article
- Development of Time-Aggregated Machine Learning Model for Relapse Prediction in Pediatric Crohn's Disease.Clinical and translational gastroenterology · 2025Article
- Electronic Health Records-based identification of newly diagnosed Crohn's Disease cases.Artificial intelligence in medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 2 institutions in 2 countries.
Funding
Abstract
BACKGROUND AND
aimsThere is increasing interest in machine learning-based prediction models in inflammatory bowel diseases [IBD]. We synthesised and critically appraised studies comparing machine learning vs traditional statistical models, using routinely available clinical data for risk prediction in IBD.
methodsThrough a systematic review till January 1, 2021, we identified cohort studies that derived and/or validated machine learning models, based on routinely collected clinical data in patients with IBD, to predict the risk of harbouring or developing adverse clinical outcomes, and reported its predictive performance against a traditional statistical model for the same outcome. We appraised the risk of bias in these studies using the Prediction model Risk of Bias ASsessment [PROBAST] tool.
resultsWe included 13 studies on machine learning-based prediction models in IBD, encompassing themes of predicting treatment response to biologics and thiopurines and predicting longitudinal disease activity and complications and outcomes in patients with acute severe ulcerative colitis. The most common machine learning models used were tree-based algorithms, which are classification approaches achieved through supervised learning. Machine learning models outperformed traditional statistical models in risk prediction. However, most models were at high risk of bias, and only one was externally validated.
conclusionsMachine learning-based prediction models based on routinely collected data generally perform better than traditional statistical models in risk prediction in IBD, though frequently have high risk of bias. Future studies examining these approaches are warranted, with special focus on external validation and clinical applicability.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.