ArticleScientific reports2021
Machine learning using clinical data at baseline predicts the efficacy of vedolizumab at week 22 in patients with ulcerative colitis.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.
- Prognostic Prediction Models for Ulcerative Colitis: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Etrasimod for the symptomatic relief of ulcerative colitis: a post-hoc analysis from the ELEVATE UC clinical programme.BMJ open gastroenterology · 2025Trial
- Artificial Intelligence for Drug Safety Across the Lifecycle and Decision Type: A Scoping Review.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Recent Advances in Molecular Targeted Therapy for Inflammatory Bowel Disease.Internal medicine (Tokyo, Japan) · 2026Review
- Identifying key clinical and biochemical predictors of treatment outcomes in inflammatory bowel disease: a real-world evidence study.Scientific reports · 2025Article
- How Artificial Intelligence Will Transform Clinical Care, Research, and Trials for Inflammatory Bowel Disease.Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association · 2025Review
- Machine Learning of Serum Cytokine and Chemokine Profiles Can Classify Inflammatory Bowel Disease Beyond Clinical Diagnosis.Gastro hep advances · 2025Article
- Artificial intelligence use for precision medicine in inflammatory bowel disease: a systematic review.American journal of translational research · 2025Review
- Machine Learning for Predicting Biologic Agent Efficacy in Ulcerative Colitis: An Analysis for Generalizability and Combination with Computational Models.Diagnostics (Basel, Switzerland) · 2024Article
- The Role of Artificial Intelligence in the Diagnosis and Treatment of Ulcerative Colitis.Diagnostics (Basel, Switzerland) · 2024Review
- Predictive, preventive and personalised approach as a conceptual and technological innovation in primary and secondary care of inflammatory bowel disease benefiting affected individuals and populations.The EPMA journal · 2024Review
- Machine learning using clinical data at baseline predicts the medium-term efficacy of ustekinumab in patients with ulcerative colitis.Scientific reports · 2024Article
- Artificial intelligence and machine learning technologies in ulcerative colitis.Therapeutic advances in gastroenterology · 2024Review
- Artificial intelligence in inflammatory bowel disease: implications for clinical practice and future directions.Intestinal research · 2023Review
- A stacking ensemble machine learning model to predict alpha-1 antitrypsin deficiency-associated liver disease clinical outcomes based on UK Biobank data.Scientific reports · 2022Article
- Predictive models for endoscopic disease activity in patients with ulcerative colitis: Practical machine learning-based modeling and interpretation.Frontiers in medicine · 2022Article
- Histological Predictors for Therapeutic Response to Integrin Inhibitors in Patients with Ulcerative Colitis.Inflammatory intestinal diseasesArticle
- Machine learning in predicting treatment response and remission in inflammatory bowel disease: a systematic review.Annals of gastroenterologyArticle
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Predicting the response of patients with ulcerative colitis (UC) to a biologic such as vedolizumab (VDZ) before administration is an unmet need for optimizing individual patient treatment. We hypothesized that the machine-learning approach with daily clinical information can be a new, promising strategy for developing a drug-efficacy prediction tool. Random forest with grid search and cross-validation was employed in Cohort 1 to determine the contribution of clinical features at baseline (week 0) to steroid-free clinical remission (SFCR) with VDZ at week 22. Among 49 clinical features including sex, age, height, body weight, BMI, disease duration/phenotype, treatment history, clinical activity, endoscopic activity, and blood test items, the top eight features (partial Mayo score, MCH, BMI, BUN, concomitant use of AZA, lymphocyte fraction, height, and CRP) were selected for logistic regression to develop a prediction model for SFCR at week 22. In the validation using the external Cohort 2, the positive and negative predictive values of the prediction model were 54.5% and 92.3%, respectively. The prediction tool appeared useful for identifying patients with UC who would not achieve SFCR at week 22 during VDZ therapy. This study provides a proof-of-concept that machine learning using real-world data could permit personalized treatment for UC.
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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.