Medical big data: promise and challenges

Abstract

The concept of big data, commonly characterized by volume, variety, velocity, and veracity, goes far beyond the data type and includes the aspects of data analysis, such as hypothesis-generating, rather than hypothesis-testing. Big data focuses on temporal stability of the association, rather than on causal relationship and underlying probability distribution assumptions are frequently not required. Medical big data as material to be analyzed has various features that are not only distinct from big data of other disciplines, but also distinct from traditional clinical epidemiology. Big data technology has many areas of application in healthcare, such as predictive modeling and clinical decision support, disease or safety surveillance, public health, and research. Big data analytics frequently exploits analytic methods developed in data mining, including classification, clustering, and regression. Medical big data analyses are complicated by many technical issues, such as missing values, curse of dimensionality, and bias control, and share the inherent limitations of observation study, namely the inability to test causality resulting from residual confounding and reverse causation. Recently, propensity score analysis and instrumental variable analysis have been introduced to overcome these limitations, and they have accomplished a great deal. Many challenges, such as the absence of evidence of practical benefits of big data, methodological issues including legal and ethical issues, and clinical integration and utility issues, must be overcome to realize the promise of medical big data as the fuel of a continuous learning healthcare system that will improve patient outcome and reduce waste in areas including nephrology.

Brief introduction of medical database and data mining technology in big data era

“…Medical data have the characteristics of disease diversity, heterogeneity of treatment and outcome, and the complexity of collecting, processing, and interpreting data 14 . With the development of medical information, a large number of digital data has been produced in the process of medical service, health care, and health management, forming medical big data 15 . Medical big data come from a variety of sources, such as administrative claims records, clinical registration, electronic health records, biometric data, patient report data, and more 16,17 .…”

Identifying the “Blip on the Radar Screen”: Leveraging Big Data in Defining Drug Safety and Efficacy in Pediatric Practice

“…These patients are far more complicated, with multiple comorbidities and concurrent therapy that are severely restricted in RCTs. RCTs often produce conflicting results that are reflective of the differences in study design that result in distinct groups of patients and study procedures, necessitating the need for multiple RCTs to establish a “gold standard.” Well‐designed observational studies may be less prone to these varied results due to a broader representation of the population at risk …”

Can Medical Research Do Its Job?

“…With over 26 million research publications in the National Library of Medicine's database (PubMed) [1], laboratory based researchers don't have enough hours in the day to do the timeconsuming relational data mining needed to understand how and where their results fit in and propose falsifiable theories of disease that arise from insight gained by studying little known areas of medicine such as redox biochemistry, bioenergetics and redox biology in addition to emerging sources of information such as Bbig data^ [2].…”