RT Journal Article
SR Electronic
T1 Big data in IBD: big progress for clinical practice
JF Gut
JO Gut
FD BMJ Publishing Group Ltd and British Society of Gastroenterology
SP 1520
OP 1532
DO 10.1136/gutjnl-2019-320065
VO 69
IS 8
A1 Nasim Sadat Seyed Tabib
A1 Matthew Madgwick
A1 Padhmanand Sudhakar
A1 Bram Verstockt
A1 Tamas Korcsmaros
A1 Séverine Vermeire
YR 2020
UL http://gut.bmj.com/content/69/8/1520.abstract
AB IBD is a complex multifactorial inflammatory disease of the gut driven by extrinsic and intrinsic factors, including host genetics, the immune system, environmental factors and the gut microbiome. Technological advancements such as next-generation sequencing, high-throughput omics data generation and molecular networks have catalysed IBD research. The advent of artificial intelligence, in particular, machine learning, and systems biology has opened the avenue for the efficient integration and interpretation of big datasets for discovering clinically translatable knowledge. In this narrative review, we discuss how big data integration and machine learning have been applied to translational IBD research. Approaches such as machine learning may enable patient stratification, prediction of disease progression and therapy responses for fine-tuning treatment options with positive impacts on cost, health and safety. We also outline the challenges and opportunities presented by machine learning and big data in clinical IBD research.