A Study on the Implementation of Big Data for Suicide Prevention Programs


  • Hye-Jung Chang
  • . .




Big data, Data classification, Suicide Prevention, Safety Data Analysis, Safety Data Pool, WHO Safety community


In a rapidly changing environment, big data is becoming important as a response capability through rapid situational awareness. The purpose of this study is to propose a suicide prevention safety program that responds to the latest increases in suicides through big data collection and analysis. To this end, I will study the process of creating a pool of big data, selecting data items that discover suicide syndrome, and developing suicide prevention programs. When I designed the suicide prevention program, the necessary data was defined and the analysis process was proposed. For this study, the big data 7 step methodology is applied. I created a big data pool for suicide prevention programs. The data used in the big data pool can be categorized into structured and unstructured data. The overviews of city, disaster, situation of injuries, and injury details use structured data, whereas injury factor can utilize either unstructured or structured data. To set up a suicide prevention program, first, the high-risk group is derived, second, the priority control target is derived, and finally, the detailed program is implemented. I suggested utilizing structured and unstructured data for effective analysis and selection of suicide prevention programs.




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