Application of Big Data analysis in gastrointestinal research

Table 5 Examples of studies on inflammatory bowel disease research by utilization of large healthcare datasets

Inflammatory bowel disease
Country/Region	Database	Area of research	Sample size	Design, statistical methods and 3V	Application
South Korea	Korean Health Insurance Review and Assessment Service (HIRA)	UC	11233	Nationwide retrospective cohort study	Incidence and clinical impact of perianal disease in UC
		Song et al[97], 2018
				Comparator: general population
				Volume, Velocity and Variety
Taiwan, China	Taiwan National Health Insurance Database (NHID)	IBD	38039	Nationwide retrospective cohort study to compare IBD patients with general population to derive SIR	Association between IBD and herpes zoster infection
		Chang et al[98], 2018
				Hospital based nested case-control study
				Volume, Velocity and Variety
Sweden	Swedish Patient Registry	UC	63711	Nationwide retrospective cohort study	Association between appendectomy and UC
		Myrelid et al[99], 2017
				Volume, Velocity and Variety
	Swedish Medical Birth Register (child-mother link)	IBD	827,239 children born between 2006 and 2013	Nationwide prospective population-based register study	Association between maternal exposure to antibiotics during pregnancy and very early onset IBD in adulthood
		Ortqvist et al[72], 2019
				Volume, Velocity and Variety
	Swedish Multigeneration Register (child-father link)
	Swedish Prescribed Drug Register National Patient Register
United States	NCBI Gene Expression Omnibus (GEO)	IBD	Not applicable	Signature inversion study	Topiramate as a potential therapeutic agent against IBD
		Dudley et al[70], 2011
				Volume, Velocity and Variety
United States	Not applicable	IBD	1585	Retrospective cohort study Natural language processing	Association between arthralgia and biologics (anti-TNF vs vedolizumab)
		Cai et al[20], 2018
				Volume, Velocity and Variety
Not applicable	International IBD Genetics Consortium's Immunochip project	IBD	53279	Machine learning algorithm	Predictors of IBD
		Wei et al[64], 2013
				Volume, Velocity and Variety
United States	Not applicable	IBD	575 colonoscopy reports	Retrospective cohort study Natural language processing	Differentiation of surveillance from non-surveillance colonoscopy
		Hou et al[100], 2013
				Volume, Velocity and Variety
United States	Not applicable	IBD	1080	Retrospective cohort study	Prediction of IBD remission in thiopurine users
		Waljee et al[66], 2017
				Random Forest machine learning algorithm
United States	Not applicable	IBD	20368	Retrospective cohort study	Prediction of hospitalization and outpatient steroid use
		Waljee et al[65], 2017
				Random Forest machine learning algorithm
Not applicable	Phase 3 clinical trial data	IBD	491	Retrospective cohort study	Prediction of steroid-free endoscopic remission with vedolizumab in UC
		Waljee et al[67], 2018
				Random Forest machine learning algorithm
				Volume, Velocity and Variety

This list is not exhaustive, but serves to provide a few distinct examples of how Big Data analysis can generate high-quality research outputs in the field of gastroenterology and hepatology. 3V: Volume/velocity/variety; UC: Ulcerative colitis; IBD: Inflammatory bowel disease; SIR: Standardized incidence ratio; anti-TNF: anti-tumour necrosis factor.