Evidence map›Paper›PMID 35476181›Full record

ArticleDigestive diseases and sciences2022

Artificial Intelligence for Inflammatory Bowel Diseases (IBD); Accurately Predicting Adverse Outcomes Using Machine Learning.

Aria Zand, Zack Stokes, Arjun Sharma, Welmoed K van Deen, Daniel Hommes

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Digestive diseases and sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
–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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

19 citing papers in PubMed.

  1. Review
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  10. Article
  11. Challenges in IBD Research 2024: Precision Medicine.Inflammatory bowel diseases · 2024
    Review
  12. Review
  13. Article
  14. Article
  15. Review
  16. Article
  17. Article
  18. Artificial Intelligence in Inflammatory Bowel Disease.Gastroenterology & hepatology · 2022
    Article
  19. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Aria ZandUCLA Center for Inflammatory Bowel Diseases, Vatche and Tamar Manoukian Division of Digestive Disease, David Geffen School of Medicine, University of California at Los Angeles, 10945 Le Conte Ave #2338, Los Angeles, CA, 90095, USA. azand89@gmail.com.ORCID 0000-0001-5157-9380
Zack StokesUCLA Center for Inflammatory Bowel Diseases, Vatche and Tamar Manoukian Division of Digestive Disease, David Geffen School of Medicine, University of California at Los Angeles, 10945 Le Conte Ave #2338, Los Angeles, CA, 90095, USA.
Arjun SharmaUCLA Center for Inflammatory Bowel Diseases, Vatche and Tamar Manoukian Division of Digestive Disease, David Geffen School of Medicine, University of California at Los Angeles, 10945 Le Conte Ave #2338, Los Angeles, CA, 90095, USA.
Welmoed K van DeenCedars-Sinai Center for Outcomes Research and Education, Cedars-Sinai Medical Center, Division of Health Services Research, Los Angeles, CA, USA.
Daniel HommesUCLA Center for Inflammatory Bowel Diseases, Vatche and Tamar Manoukian Division of Digestive Disease, David Geffen School of Medicine, University of California at Los Angeles, 10945 Le Conte Ave #2338, Los Angeles, CA, 90095, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundInflammatory Bowel Diseases with its complexity and heterogeneity could benefit from the increased application of Artificial Intelligence in clinical management.

aimTo accurately predict adverse outcomes in patients with IBD using advanced computational models in a nationally representative dataset for potential use in clinical practice.

methodsWe built a training model cohort and validated our result in a separate cohort. We used LASSO and Ridge regressions, Support Vector Machines, Random Forests and Neural Networks to balance between complexity and interpretability and analyzed their relative performances and reported the strongest predictors to the respective models. The participants in our study were patients with IBD selected from The OptumLabs® Data Warehouse (OLDW), a longitudinal, real-world data asset with de-identified administrative claims and electronic health record (EHR) data.

resultsWe included 72,178 and 69,165 patients in the training and validation set, respectively. In total, 4.1% of patients in the validation set were hospitalized, 2.9% needed IBD-related surgeries, 17% used long-term steroids and 13% of patients were initiated with biological therapy. Of the AI models we tested, the Random Forest and LASSO resulted in high accuracies (AUCs 0.70-0.92). Our artificial neural network performed similarly well in most of the models (AUCs 0.61-0.90).

conclusionsThis study demonstrates feasibility of accurately predicting adverse outcomes using complex and novel AI models on large longitudinal data sets of patients with IBD. These models could be applied for risk stratification and implementation of preemptive measures to avoid adverse outcomes in a clinical setting.

Indexed as

Artificial IntelligenceInflammatory Bowel DiseasesCohort StudiesElectronic Health RecordsHumansMachine LearningArtificial intelligenceBig dataInflammatory bowel diseasesMachine learningPrecision medicine

Identifiers

PMID35476181
PMCPMC9515047

What OpenQuestion holds

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Registered trials

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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.