ArticleInternational journal of colorectal disease2015
Seasonal variation in onset and relapse of IBD and a model to predict the frequency of onset, relapse, and severity of IBD based on artificial neural network.
Article in International journal of colorectal disease, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 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
23 citing papers in PubMed, 2 syntheses or guidelines pooled it, 43 citations in OpenAlex.
- Machine Learning-based Prediction Models for Diagnosis and Prognosis in Inflammatory Bowel Diseases: A Systematic Review.Journal of Crohn's & colitis · 2022Pooled it
- Pooled it
- Role of MRI radiomics in deep learning-based prediction of intestinal diseases.International journal of colorectal disease · 2026Review
- Artificial intelligence in ophthalmology clinical trials: a narrative review.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Review
- Modifiable Factors Influencing Disease Flares in Inflammatory Bowel Disease: A Literature Overview of Lifestyle, Psychological, and Environmental Risk Factors.Journal of clinical medicine · 2025Review
- Development and Validation of Predictive Models for Inflammatory Bowel Disease Diagnosis: A Machine Learning and Nomogram-Based Approach.Journal of inflammation research · 2025Article
- Development of Time-Aggregated Machine Learning Model for Relapse Prediction in Pediatric Crohn's Disease.Clinical and translational gastroenterology · 2025Article
- Integration of machine learning to identify diagnostic genes in leukocytes for acute myocardial infarction patients.Journal of translational medicine · 2023Article
- The Constrained-Disorder Principle Assists in Overcoming Significant Challenges in Digital Health: Moving from "Nice to Have" to Mandatory Systems.Clinics and practice · 2023Review
- Identification of useful genes from multiple microarrays for ulcerative colitis diagnosis based on machine learning methods.Scientific reports · 2022Article
- Machine Learning Predictive Outcomes Modeling in Inflammatory Bowel Diseases.Inflammatory bowel diseases · 2022Review
- Application of Artificial Intelligence to Clinical Practice in Inflammatory Bowel Disease - What the Clinician Needs to Know.Journal of Crohn's & colitis · 2022Article
- Multiple-model machine learning identifies potential functional genes in dilated cardiomyopathy.Frontiers in cardiovascular medicine · 2022Article
- Challenges and opportunities in the application of artificial intelligence in gastroenterology and hepatology.World journal of gastroenterology · 2021Review
- Status quo and future prospects of artificial neural network from the perspective of gastroenterologists.World journal of gastroenterology · 2021Review
- Artificial Intelligence Enhances Studies on Inflammatory Bowel Disease.Frontiers in bioengineering and biotechnology · 2021Review
- Insolation and Disease Severity in Paediatric Inflammatory Bowel Disease-A Multi-Centre Cross-Sectional Study.Journal of clinical medicine · 2020Article
- Article
- Artificial Intelligence in Medicine: Today and Tomorrow.Frontiers in medicine · 2020Article
- Applying Machine Learning Models to Predict Medication Nonadherence in Crohn's Disease Maintenance Therapy.Patient preference and adherence · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPrevious research has yielded conflicting data as to whether the natural history of inflammatory bowel disease follows a seasonal pattern. The purpose of this study was (1) to determine whether the frequency of onset and relapse of inflammatory bowel disease follows a seasonal pattern and (2) to establish a model to predict the frequency of onset, relapse, and severity of inflammatory bowel disease (IBD) with meteorological data based on artificial neural network (ANN).
methodPatients with diagnosis of ulcerative colitis (UC) or Crohn's disease (CD) between 2003 and 2011 were investigated according to the occurrence of onset and flares of symptoms. The expected onset or relapse was calculated on a monthly basis over the study period. For artificial neural network (ANN), patients from 2003 to 2010 were assigned as training cohort and patients in 2011 were assigned as validation cohort. Mean square error (MSE) and mean absolute percentage error (MAPE) were used to evaluate the predictive accuracy.
resultsWe found no seasonal pattern of onset (P = 0.248) and relapse (P = 0.394) among UC patients. But, the onset (P = 0.015) and relapse (P = 0.004) of CD were associated with seasonal pattern, with a peak in July and August. ANN had average accuracy to predict the frequency of onset (MSE = 0.076, MAPE = 37.58%) and severity of IBD (MSE = 0.065, MAPE = 42.15%) but high accuracy in predicting the frequency of relapse of IBD (MSE = 0.009, MAPE = 17.1%).
conclusionThe frequency of onset and relapse in IBD showed seasonality only in CD, with a peak in July and August, but not in UC. ANN may have its value in predicting the frequency of relapse among patients with IBD.
Indexed as
Identifiers
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.