Evidence map›Paper›PMID 32111636›Full record

ReviewGut2020

Big data in IBD: big progress for clinical practice.

Nasim Sadat Seyed Tabib, Matthew Madgwick, Padhmanand Sudhakar, Bram Verstockt, Tamas Korcsmaros, Séverine Vermeire

Registry-linked trialAbstract readReview
In one paragraph

Review in Gut, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06512792 (Mobile App-Based Remote Management Model for IBD Patients), which is not on this map. Cited by 129 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
129citing papers in PubMed, 3 pooled it
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

NCT06512792 naunknown statusnot on this mapstarted 2024, after this paper: background citation

Mobile App-Based Remote Management Model for IBD Patients: A Multi-Center Randomized Controlled Study

TypeinterventionalSponsorChanghai HospitalRan2024 to 2025Enrolled500ConditionsInflammatory Bowel DiseasesArmsTelemedicine management through Mobile APP
3 · Its place in the literature

Who cites it

129 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Guideline
  2. Pooled it
  3. Pooled it
  4. Trial
  5. Review
  6. Article
  7. Article
  8. Dietary Patterns and Food Choices of Adults With Inflammatory Bowel Disease in New Zealand.JGH open : an open access journal of gastroenterology and hepatology · 2026
    Article
  9. Article
  10. Review
  11. Article
  12. Review
  13. New Technologies for IBD Endoscopy.Journal of clinical medicine · 2026
    Review
  14. Article
  15. Review
  16. Inhibiting METTL3 in macrophages alleviates colitis by enhancing M2 polarization via upregulating IRAKM.Inflammation research : official journal of the European Histamine Research Society ... [et al.] · 2026
    Article
  17. Article
  18. Loss of LXRβ Drives CD4Biomedicines · 2026
    Article
  19. Article
  20. Review

69 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Nasim Sadat Seyed TabibDepartment of Chronic Diseases, Metabolism and Ageing, TARGID, KU Leuven, Leuven, Belgium.ORCID 0000-0001-9612-0012
Matthew MadgwickOrganisms and Ecosystems, Earlham Institute, Norwich, UK.
Padhmanand SudhakarDepartment of Chronic Diseases, Metabolism and Ageing, TARGID, KU Leuven, Leuven, Belgium.
Bram VerstocktTranslational Research in GastroIntestinal Disorders, KU Leuven, Leuven, Belgium.ORCID 0000-0003-3898-7093
Tamas KorcsmarosOrganisms and Ecosystems, Earlham Institute, Norwich, UK.
Séverine VermeireDepartment of Chronic Diseases, Metabolism and Ageing, TARGID, KU Leuven, Leuven, Belgium severine.vermeire@uzleuven.be.

Funding

Biotechnology and Biological Sciences Research Council BB/CSP17270/1Biotechnology and Biological Sciences Research Council BB/J004529/1Biotechnology and Biological Sciences Research Council BB/P016774/1Biotechnology and Biological Sciences Research Council BB/S50743X/1Biotechnology and Biological Sciences Research Council BBS/E/F/000PR10355Biotechnology and Biological Sciences Research Council BBS/E/T/000PR9817
6 · The paper itself

Abstract

IBD is a complex multifactorial inflammatory disease of the gut driven by extrinsic and intrinsic factors, including host genetics, the immune system, environmental factors and the gut microbiome. Technological advancements such as next-generation sequencing, high-throughput omics data generation and molecular networks have catalysed IBD research. The advent of artificial intelligence, in particular, machine learning, and systems biology has opened the avenue for the efficient integration and interpretation of big datasets for discovering clinically translatable knowledge. In this narrative review, we discuss how big data integration and machine learning have been applied to translational IBD research. Approaches such as machine learning may enable patient stratification, prediction of disease progression and therapy responses for fine-tuning treatment options with positive impacts on cost, health and safety. We also outline the challenges and opportunities presented by machine learning and big data in clinical IBD research.

Indexed as

Big DataGenomicsMachine LearningGastrointestinal MicrobiomeGene Expression ProfilingHumansImage Interpretation, Computer-AssistedInflammatory Bowel DiseasesMetagenomicsPrecision MedicinePrognosisProteomicsRisk AssessmentTranslational Research, BiomedicalCrohn's diseaseIBDulcerative colitis

Identifiers

PMID32111636
PMCPMC7398484

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.