Application of Big Data analysis in gastrointestinal research

作     者：Ka-Shing Cheung Wai K Leung Wai-Kay Seto 

作者机构：Department of MedicineThe University of Hong KongQueen Mary HospitalHong KongChina Department of MedicineThe University of Hong Kong- Shenzhen HospitalShenzhen 518053Guangdong ProvinceChina 

出 版 物：《World Journal of Gastroenterology》 (世界胃肠病学杂志（英文版）)

年 卷 期：2019年第25卷第24期

页      面：2990-3008页

核心收录：

学科分类：1002[医学-临床医学] 10[医学] 

基　　金：Gilead Sciences Ipsen Biopharmaceuticals 

主　　题：Healthcare dataset Epidemiology Gastric cancer Inflammatory bowel disease Colorectal cancer Hepatocellular carcinoma Gastrointestinal bleeding 

摘      要：Big Data,which are characterized by certain unique traits like volume,velocity and value,have revolutionized the research of multiple fields including *** Data in health care are defined as large datasets that are collected routinely or automatically,and stored *** 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 *** application of Big Data analysis in the field of gastroenterology and hepatology research has also opened new research *** 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 *** randomized controlled trials,it reflects the real-world situation and studies patients who are often under-represented in randomized controlled ***,residual and/or unmeasured confounding remains a major concern,which requires meticulous study design and various statistical adjustment *** potential drawbacks include data validity,missing data,incomplete data capture due to the unavailability of diagnosis codes for certain clinical situations,and individual *** continuous technological advances,some of the current limitations with Big Data may be further *** review will illustrate the use of Big Data research on gastrointestinal and liver diseases using recently published examples.