# Medical Application of Big Data: Between Systematic Review and Randomized Controlled Trials
## 2 Definition and Types of Big Data in Medical Health Area  
Big data can be explained in two ways [4]: first, for technology itself, it means analyzing skills to cover huge data in ways that would previously have been impossible with classic analytic tools; second, for huge data in itself. Big data can be categorized into classical (structured) and non-classical (unstructured) data. Non-classical data without characteristics means non-numerical data including pictures, sound, words, etc. The main purposes of using big data are to make policies for public health, medical institutions, business, and research. However, big data also involves the risk of it being used in ways other than its intended purpose.  
Healthcare big data includes medical data, genomic data, and life-log data generated by humans during their lives, and it is scattered along a very wide range and on a huge scale (Figure 1), so it is essential to build an integrated platform that considers precise measurement, transmission, storage, and security methods for the proper use of such data [5; 6]. Healthcare complexity arises due to the range of health conditions and their co-morbidities, varied treatments and outcomes, and intricate study designs, analytical methods, and data interpretation approaches in healthcare data management [7]. As a result, the role of domain knowledge can be dominant in both data analysis and interpretation of results [8]. There have been many researchers' definitions of medical big data, and some have categorized medical big data based on who owns the data compared to traditional clinical data [9]. Basically, medical big data is often difficult to access, and most researchers in the medical field are hesitant to share their data due to the risk of data misuse by the other parties [10]. In addition, medical big data is relatively structured because it adheres to protocols regarding the collection of an individual's medical information [11].  
[PERSON] and [PERSON] [12] well-summarized medical big data versus traditional classical statistical analysis. The main difference between the two data types is that traditional classical statistical analysis focuses on hypothesis testing, while medical big data analysis focuses on hypothesis generating. In addition, the research question is characterized by the fact that traditional classical statistical analysis is conducted to interpret the causal relationship, while medical big data analysis focuses on the correlation between variables or identification of specific patterns [12]. In fact, the power of big data lies in identifying correlations, not necessarily in establishing the significance or meaning of these correlations [13].  
Figure 1: Definition Types of big data related to healthcare and types of big data in the medical health area.  
The potential value of medical big data has been demonstrated in: (1) predictive modelling for risk and resource use; (2) population management; (3) drug and medical device safety surveillance; (4) disease and treatment heterogeneity; (5) precision medicine and clinical decision support; (6) quality of care and performance measurement; (7) public health; and (8) research applications [14]. It is expected that the analysis of healthcare big data using artificial intelligence will facilitate the identification of specific patterns of diseases that we want to know about as well as the prevention, management, and treatment of diseases.