Machine Learning-based Prediction Models for Diagnosis and Prognosis in Inflammatory Bowel Diseases: A Systematic Review

Nghia Nguyen; Dominic Picetti; Parambir S. Dulai; Vipul Jairath; William J. Sandborn; Lucila Ohno‐Machado; Peter L. Chen; Siddharth Singh

doi:10.1093/ecco-jcc/jjab155

September 7, 2021Journal of Crohn s and ColitisOpen Access

Machine Learning-based Prediction Models for Diagnosis and Prognosis in Inflammatory Bowel Diseases: A Systematic Review

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Key Points

To evaluate the effectiveness of machine learning models in predicting clinical outcomes in inflammatory bowel diseases compared to traditional statistical models.
Systematic review of studies up to January 1, 2021
Cohort studies using clinical data to derive or validate machine learning models
Risk of bias assessment using the PROBAST tool
Thirteen studies on machine learning prediction models in IBD included
Machine learning models outperformed traditional statistical models in risk prediction
High risk of bias found in most studies, with only one model validated externally.

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Implication

Systematic review compares machine learning and traditional models for predicting outcomes in inflammatory bowel diseases, indicating better performance but high bias risks.

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Cite This Study

Nguyen et al. (2021) studied this question.

synapsesocial.com/papers/6a159246d64fa333899fd37f https://doi.org/10.1093/ecco-jcc/jjab155

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Machine Learning-based Prediction Models for Diagnosis and Prognosis in Inflammatory Bowel Diseases: A Systematic Review

Key Points

No abstract yet

Implication

Discussion

Cite This Study

Machine Learning-based Prediction Models for Diagnosis and Prognosis in Inflammatory Bowel Diseases: A Systematic Review

Key Points

No abstract yet

Implication

Discussion

Cite This Study

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