Evidence map›Paper›PMID 34417815›Full record

ReviewInflammatory bowel diseases2022

Machine Learning Predictive Outcomes Modeling in Inflammatory Bowel Diseases.

Aamir Javaid, Omer Shahab, William Adorno, Philip Fernandes, Eve May, Sana Syed

Open access · greenAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Inflammatory bowel diseases, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed
1.3field-weighted citation impact, top 16% of its field
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

26 citing papers in PubMed, 32 citations in OpenAlex.

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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 at 3 institutions in 1 country.

Aamir JavaidDivision of Pediatric Gastroenterology and Hepatology, Department of Pediatrics, University of Virginia, Charlottesville, VA, USA.
Omer ShahabDivision of Gastroenterology and Hepatology, Department of Medicine, Virginia Commonwealth University, Richmond, VA, USA.
William AdornoSchool of Data Science, University of Virginia, Charlottesville, VA, USA.
Philip FernandesDivision of Pediatric Gastroenterology and Hepatology, Department of Pediatrics, University of Virginia, Charlottesville, VA, USA.
Eve MayDivision of Gastroenterology and Hepatology, Department of Pediatrics, Children's National Hospital, Washington, DC, USA.
Sana SyedDivision of Pediatric Gastroenterology and Hepatology, Department of Pediatrics, University of Virginia, Charlottesville, VA, USA.ORCID 0000-0003-0954-0583
University of Virginia · USChildren's National · USVirginia Commonwealth University · US

Funding

Computational Characterization of Environmental EnteropathyK23DK117061 · NIDDK · UNIVERSITY OF VIRGINIA · PI SYED, SANA · 2019 to 2023
$964k
NIDDK NIH HHS K23 DK117061
6 · The paper itself

Abstract

There is a rising interest in use of big data approaches to personalize treatment of inflammatory bowel diseases (IBDs) and to predict and prevent outcomes such as disease flares and therapeutic nonresponse. Machine learning (ML) provides an avenue to identify and quantify features across vast quantities of data to produce novel insights in disease management. In this review, we cover current approaches in ML-driven predictive outcomes modeling for IBD and relate how advances in other fields of medicine may be applied to improve future IBD predictive models. Numerous studies have incorporated clinical, laboratory, or omics data to predict significant outcomes in IBD, including hospitalizations, outpatient corticosteroid use, biologic response, and refractory disease after colectomy, among others, with considerable health care dollars saved as a result. Encouraging results in other fields of medicine support efforts to use ML image analysis-including analysis of histopathology, endoscopy, and radiology-to further advance outcome predictions in IBD. Though obstacles to clinical implementation include technical barriers, bias within data sets, and incongruence between limited data sets preventing model validation in larger cohorts, ML-predictive analytics have the potential to transform the clinical management of IBD. Future directions include the development of models that synthesize all aforementioned approaches to produce more robust predictive metrics.

Indexed as

Inflammatory Bowel DiseasesBiasHospitalizationHumansMachine LearningPrognosisartificial intelligenceinflammatory bowel diseasesmachine learningpredictive modeling

Identifiers

PMID34417815
PMCPMC9165557
OpenAlexW3195179678

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.