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A Prediction of Number of Patients and Risk of Disease in Each Region Based on Pharmaceutical Prescription Data

의약품 처방 데이터 기반의 지역별 예상 환자수 및 위험도 예측

  • Accepted : 2018.01.24
  • Published : 2018.02.28

Abstract

Recently, big data has been growing rapidly due to the development of IT technology. Especially in the medical field, big data is utilized to provide services such as patient-customized medical care, disease management and disease prediction. In Korea, 'National Health Alarm Service' is provided by National Health Insurance Corporation. However, the prediction model has a problem of short-term prediction within 3 days and unreliability of social data used in prediction model. In order to solve these problems, this paper proposes a disease prediction model using medicine prescription data generated from actual patients. This model predicts the total number of patients and the risk of disease in each region and uses the ARIMA model for long-term predictions.

Acknowledgement

Supported by : Ministry of Education in Korea

References

  1. T. Song, "Social Big Data and Its Application: With Special Reference to MERS Information Diffusion and Risk Prediction," Health and Welfare Policy Forum of Korea, Vol. 227, pp. 29-49, 2015.
  2. T. Song, "Big Data Trend and Utilization Plan for Korean Health and Welfare," Science and Technology Policy, Vol. 192, pp. 56-73, 2013.
  3. J. Chang, Y. Kim, J. Choi, C. Kim, A. and Nasridinov, "A Study on Medicine Prescription Data Based Disease Occurrence Predictions," Proceedings of the Korean Database Conference, pp. 118-121, 2017.
  4. J. Yoon, S. Kim, B. Lee and B. Hwang, “A Correlation Analysis between the Social Signals of Cold Symptoms Extracted from Twitter and the Influence Factors,” Journal of Korea Multimedia Society, Vol. 15, No. 6, pp. 667-677, 2013.
  5. M. Kim, Y. Yu, and B. Min, "Development of Bigdata Application System Using Complex Event Processing Technology for Medical Institution," Journal of Korean Institute of Information Technology, Vol. 14, No. 2, pp. 99-106. 2016.
  6. S. Kim and H. Hwang, "Developing a Personalized Disease and Hospital Information Application Using Medical Big Data," Entrue Jounal of Information Technology, Vol. 15, No. 2, pp. 7-16, 2016.
  7. E. Hwang, Daily-based Prediction Models for Influenza Disease Using Social Big Data, Master's Thesis of Chungbuk University, 2015.
  8. G. Kim, U. Kim, S. No, D. Lim, and J. Jeong, "Implementation of Disease Prediction and Health Info Application Based on Climate and Disease Big Data," Proceedings of the KITT Summer Conference, pp. 496-497, 2017.
  9. Complete Guide To Create A Time Series Forecast With Python, https://www.analyticsvidhya.com/blog/2016/02/time-series-forecasting-codes-python, (accessed Dec., 23, 2017).
  10. Dickey-Fuller Test, https://en.wikipedia.org/wiki/Dickey%E2%80%93Fuller_test, (accessed Dec., 27, 2017).
  11. Time Series Model, http://env1.kangwon.ac.kr/leakage/2009/management/research/knowledge/04/Jang/%EC%8B%9C%EA%B3%84%EC%97%B4%20%EB%AA%A8%ED%98%95_Time%20Series%20Model.hwp, (accessed Dec., 30, 2017).
  12. RMSE: Root Mean Square Error, http://www.statisticshowto.com/rmse/, (accessed Feb., 10, 2018).