Machine Learning Predictive Outcomes Modeling in Inflammatory Bowel Diseases

• Published in: Inflammatory bowel diseases Vol. 28; no. 6; pp. 819 - 829
• Main Authors: Javaid, Aamir, Shahab, Omer, Adorno, William, Fernandes, Philip, May, Eve, Syed, Sana
• Format: Journal Article
• Language: English
• Published: US Oxford University Press 03.06.2022
• Subjects: Bias; Editor's Choice; Hospitalization; Humans; Inflammatory Bowel Diseases - drug therapy; Leading Off; Machine Learning; Prognosis; inflammatory bowel diseases; machine learning; predictive modeling; artificial intelligence
• ISSN: 1078-0998; 1536-4844
• DOI: 10.1093/ibd/izab187

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.