# Medical Application of Big Data: Between Systematic Review and Randomized Controlled Trials
## 6 Big Data Research Design  
Big data analytics is largely based on traditional statistical analysis and machine learning (ML). The boundaries between statistical inference and ML are debatable, but while some methods fall into one or the other, many are used in both [25]. Statistics focuses on making inferences to prove a specific hypothesis, while ML is about finding generalizable patterns of prediction [26]. Big data with more variables (features) than observed data leads to an increase in dimensionality, which means that there are more variables to control, but the observed data is relatively limited, and there are more and more empty spaces between the variables due to limited data, so the performance of the entire model will eventually decrease [7; 27]. In other words, it is a major concern in big data analysis to consider the decrease in model performance caused by the curse of dimensionality compared to the actual benefit of increasing data. In general, the overfitting of the model due to too many variables causes the problem of generalization, so the curse of dimensionality is solved by reducing the dimensionality [28] or selecting the variables [29]. The most important factor in big data research is applying the right methodology. So how do we apply the right methodology? The answer lies in SR. We conduct SR to know the past of a certain study, study using big data to understand the current trend, and apply RCT to study preventive drugs or treatment that will be needed in the future. Conducting SR allows one to consider the design of all studies on the subject and know the strengths and weaknesses of each. Moreover, thorough qualitative evaluation of previous studies--whether they be observational studies or RCTs--can teach one how to overcome the shortcomings of such studies. Fortunately, disease codes are standardized and commonly used worldwide, and many papers using insurance data have been introduced worldwide, which is of great help in establishing research methodologies. Here we suggest the important Q/As:  
Q: What is the first step to perform a research using medical health claimed data? A: Choose the data base you want to use. Incidence and mortality rates of specific diseases, or complications rates, etc. can be progressed through individual database access. However, if you plan a retrospective cohort study observed over a long follow-up period, it is convenient to use a platform that integrates multiple databases. Q: Are there any restrictions on individual database access? Or are there any restrictions on access to the platform? \begin{table}
\begin{tabular}{l c c} \hline \hline
**Disease** & **Disease Code** & **Specific Code** \\ \hline Chronic renal failure & & V001, V003 \\ End stage renal disease with dialysis & & \\ Blood clotting disorders (e.g., hemophilia) & & \\ Acquired clotting factor deficiency & D68.4 & V284 \\ Organ transplantation & & V013, V014, V015, V005, \\ Liver, kidney, lung, heart, pancreas, & & V277, V278 \\ small bowel transplantation & & \\ Psychiatric disease & & \\ Schizophrenia (81 cases) & F20.0 & V161 \\ Specific Infection & & V103, V124, V131, V140, \\  & & V142, V162, V170, V201, \\  & & V223, V237, V279, V280, \\ Specific encephalitis (118 cases) & A81.1 & V282, V283, V285, V286, \\  & & V287, V288, V289, V290 \\  & & \\   Early onset Alzheimer’s dementia (14 cases) & F00.0 & V800 \\   Dementia & & \\   Late onset Alzheimer’s dementia (12 cases) & F00.1 & V810 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Severe chronic incurable diseases subject to special calculation. A: In Korea, access for research purposes is allowed for non-commercial research in accordance with the Health and Medical Technology Promotion Act. This is applied to both individual data access and platforms, and in the case of foreign countries, use access is determined after protocol review in the case of research purposes. Q: Why is SR necessary for research using medical claims data?