ArticleJournal of clinical medicine2021
Machine Learning Prediction Model for Inflammatory Bowel Disease Based on Laboratory Markers. Working Model in a Discovery Cohort Study.
Article in Journal of clinical medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
36 citing papers in PubMed, 1 synthesis or guideline pooled it, 39 citations in OpenAlex.
- Risk Factors and Predictive Biomarkers for Postoperative Complications in Crohn's Disease Surgery: Systematic Review.International journal of molecular sciences · 2026Pooled it
- Understanding complex analytical data by a supervised correlation coefficient obtained from random forest.Analytical and bioanalytical chemistry · 2026Review
- Beyond ICD-10 Codes: A Multi-class Machine-Learning Approach to Identify True IBD Cases in Electronic Medical Records.Digestive diseases and sciences · 2026Article
- Red Cell Distribution Width as an Independent Marker of Objective Disease Activity in Ulcerative Colitis and Crohn's Disease: A Large Real-World Cohort of 1000 Patients.The Turkish journal of gastroenterology : the official journal of Turkish Society of Gastroenterology · 2026Article
- Artificial Intelligence in Population-Level Gastroenterology and Hepatology: A Comprehensive Review of Public Health Applications and Quantitative Impact.Digestive diseases and sciences · 2026Review
- A data-driven universal gut microbiome health assessment: a machine learning framework trained on large metagenomic data.Frontiers in microbiology · 2026Article
- From dysbiosis to precision medicine: targeting the microbial-metabolic axis in IBD management.Frontiers in cellular and infection microbiology · 2026Review
- AQP9 and IFITM1 as drivers of immune infiltration and tumor progression in IBD-associated colorectal cancer: from computational insights to experimental validation.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- IgG4 unveiled: navigating the interplay with Crohn's disease - from immunology insights to machine learning.Annals of medicine and surgery (2012) · 2025Review
- Perception and Understanding of Artificial Intelligence Among Gastroenterology Fellows and Early Career Gastroenterologists: A Nationwide Cross-Sectional Survey Study.Digestive diseases and sciences · 2025Article
- Artificial Intelligence in Advancing Inflammatory Bowel Disease Management: Setting New Standards.Cancers · 2025Review
- 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
- Pathways to chronic disease detection and prediction: Mapping the potential of machine learning to the pathophysiological processes while navigating ethical challenges.Chronic diseases and translational medicine · 2025Review
- The Role of DNA Methylation in Gastrointestinal Disease: An Expanded Review of Malignant and Nonmalignant Gastrointestinal Diseases.Gastroenterology · 2025Review
- Identifying effective immune biomarkers in alopecia areata diagnosis based on machine learning methods.BMC medical informatics and decision making · 2025Article
- Artificial intelligence use for precision medicine in inflammatory bowel disease: a systematic review.American journal of translational research · 2025Review
- Development and Validation of Predictive Models for Inflammatory Bowel Disease Diagnosis: A Machine Learning and Nomogram-Based Approach.Journal of inflammation research · 2025Article
- Digital biomarkers and artificial intelligence: a new frontier in personalized management of inflammatory bowel disease.Frontiers in immunology · 2025Review
- Differential diagnosis of eczema and psoriasis using routine clinical data and machine learning: development of a web-based tool in a multicenter outpatient cohort.Frontiers in medicine · 2025Article
- Artificial intelligence-based multimodal model for the identification of ulcerative colitis with concomitant cytomegalovirus colitis.Therapeutic advances in gastroenterology · 2025Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Inflammatory bowel disease (IBD) is a chronic, incurable disease involving the gastrointestinal tract. It is characterized by complex, unclear pathogenesis, increased prevalence worldwide, and a wide spectrum of extraintestinal manifestations and comorbidities. Recognition of IBD remains challenging and delays in disease diagnosis still poses a significant clinical problem as it negatively impacts disease outcome. The main diagnostic tool in IBD continues to be invasive endoscopy. We aimed to create an IBD machine learning prediction model based on routinely performed blood, urine, and fecal tests. Based on historical patients' data (702 medical records: 319 records from 180 patients with ulcerative colitis (UC) and 383 records from 192 patients with Crohn's disease (CD)), and using a few simple machine learning classificators, we optimized necessary hyperparameters in order to get reliable few-features prediction models separately for CD and UC. Most robust classificators belonging to the random forest family obtained 97% and 91% mean average precision for CD and UC, respectively. For comparison, the commonly used one-parameter approach based on the C-reactive protein (CRP) level demonstrated only 81% and 61% average precision for CD and UC, respectively. Results of our study suggest that machine learning prediction models based on basic blood, urine, and fecal markers may with high accuracy support the diagnosis of IBD. However, the test requires validation in a prospective cohort.
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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.