Artificial Intelligence for Inflammatory Bowel Diseases (IBD); Accurately Predicting Adverse Outcomes Using Machine Learning
Digestive Diseases and Sciences, vol. 67, pp. 4874–4885
Abstract
BACKGROUND: Inflammatory Bowel Diseases with its complexity and heterogeneity could benefit from the increased application of Artificial Intelligence in clinical management. AIM: To accurately predict adverse outcomes in patients with IBD using advanced computational models in a nationally representative dataset for potential use in clinical practice. METHODS: We 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. RESULTS: We 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). CONCLUSIONS: This 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.
Authors 5
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University of California, Los Angeles · Leiden University Medical Center · Optum (United States)
Affiliation as printed
Department of Digestive Diseases, Leiden University Medical Center, Leiden, The Netherlands. azand89@gmail.com
OptumLabs Visiting Fellow, Eden Prairie, MN, USA. azand89@gmail.com
UCLA 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
UCLA 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
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University of California, Los Angeles · Optum (United States)
Affiliation as printed
OptumLabs Visiting Fellow, Eden Prairie, MN, USA
UCLA 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
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University of California, Los Angeles
Affiliation as printed
UCLA 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
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Affiliation as printed
Cedars-Sinai Center for Outcomes Research and Education, Cedars-Sinai Medical Center, Division of Health Services Research, Los Angeles, CA, USA
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University of California, Los Angeles · Leiden University Medical Center
Affiliation as printed
Department of Digestive Diseases, Leiden University Medical Center, Leiden, The Netherlands
UCLA 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
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