Department of Medicine, The University of Hong Kong, Queen Mary Hospital, Hong Kong, China.
World J Gastroenterol. 2019 Jun 28;25(24):2990-3008. doi: 10.3748/wjg.v25.i24.2990.
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.
大数据具有体量巨大、速度快、类型多、价值密度低、真实性低等特点，已经彻底改变了包括医学在内的多个领域的研究方式。医疗保健领域的大数据是指通过常规或自动方式收集并以电子方式存储的大型数据集。随着健康数据收集量的迅速增加，可以预见大数据方法不仅可以改善个人健康，还可以提高医疗保健系统的性能。大数据分析在胃肠病学和肝病学研究领域的应用也开辟了新的研究途径。虽然它保留了传统观察性研究（病例对照和前瞻性队列研究）的大部分优势并避免了一些劣势，但它允许对疾病异质性进行表型映射，增强药物安全性，并开发精准医学、预测模型和个性化治疗。与随机对照试验不同，它反映了真实世界的情况，研究的是在随机对照试验中经常代表性不足的患者。然而，残留的和/或未测量的混杂仍然是一个主要关注点，这需要精心的研究设计和各种统计调整方法。其他潜在的缺点包括数据有效性、数据缺失、由于某些临床情况下无法获得诊断代码而导致的数据捕获不完整，以及个人隐私。随着技术的不断进步，一些当前的大数据限制可能会进一步缩小。本综述将使用最近发表的示例来说明大数据研究在胃肠道和肝脏疾病中的应用。
