Application of Big Data analysis in gastrointestinal research

Abstract

Big Data, which are characterized by certain unique traits like volume, velocity and value, have revolutionized the research of multiple fields including medicine. Big Data in health care are defined as large datasets that are collected routinely or automatically, and stored electronically. With the rapidly expanding volume of health data collection, it is envisioned that the Big Data approach can improve not only individual health, but also the performance of health care systems. The application of Big Data analysis in the field of gastroenterology and hepatology research has also opened new research approaches. While it retains most of the advantages and avoids some of the disadvantages of traditional observational studies (case-control and prospective cohort studies), it allows for phenomapping of disease heterogeneity, enhancement of drug safety, as well as development of precision medicine, prediction models and personalized treatment. Unlike randomized controlled trials, it reflects the real-world situation and studies patients who are often under-represented in randomized controlled trials. However, residual and/or unmeasured confounding remains a major concern, which requires meticulous study design and various statistical adjustment methods. Other potential drawbacks include data validity, missing data, incomplete data capture due to the unavailability of diagnosis codes for certain clinical situations, and individual privacy. With continuous technological advances, some of the current limitations with Big Data may be further minimized. This review will illustrate the use of Big Data research on gastrointestinal and liver diseases using recently published examples.

Keywords: Healthcare dataset; Epidemiology; Gastric cancer; Inflammatory bowel disease; Colorectal cancer; Hepatocellular carcinoma; Gastrointestinal bleeding

Core tip: Digital collection and storage of data has led to the generation of Big Data. Big Data analysis in the field of gastroenterology and hepatology allows for phenomapping due to disease heterogeneity (e.g., inflammatory bowel disease, gastrointestinal and liver cancers) and hence the development of precision medicine, enhances in drug safety and faster drug discovery. It has also revolutionized clinical study approaches. Although there are still limitations to Big Data approaches, some of them may be further minimized with continuous technological advances.