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머신러닝에 의한 기상상태와 COVID-19 감염 관계 분석
이재현(JaeHyun Lee),지승연(SeungYeon Ji),최원준(Wonjun Choi),강태훈(TaeHoon Kang),김상현(SangHyeon Kim),최지우(JiWoo Choi),강태원(TaeWon Kang) 한국정보기술학회 2021 Proceedings of KIIT Conference Vol.2021 No.11
COVID-19 사태의 심각성과 함께 역학조사의 중요성이 시간이 지난 지금까지도 대두되고 있다. 본 연구는 효과적인 역학조사를 위해 활동량에 영향을 끼치는 요인 중 기상정보를 선택하여 확진자의 발생 여부와 기상정보의 상관관계를 머신러닝 모델을 사용하여 분석하고자 한다. 사용한 모델은 SVM(Support Vector Machine) 으로 기본적인 기상정보와 추가적으로 추출한 특성들을 입력으로 다음날의 확진자의 발생 여부를 예측했으며 정확도 81.65%, 정밀도 98%, 재현율 71%의 성능을 보였다. 결과, 유의미한 성능향상을 보였던 대표적인 특성은 일정 기간동안의 확진자 수, 요일이 있었으며 기상정보의 경우 추가적인 특성추출에도 의미있는 성능향상을 보이지 않아 확진자 발생 여부와 기상정보의 관계가 뚜렷하지 않다는 것을 확인할 수 있었다. Along with the seriousness of the COVID-19 incident, the importance of epidemiological investigations is still emerging over time. This study aims to analyze the correlation between the occurrence of confirmed patients and weather information by selecting weather information among the factors influencing the amount of activity for effective epidemiological investigation using a machine learning model. The model used was SVM (Support Vector Machine), which predicted the occurrence of confirmed cases the next day by inputting basic weather information and additional extracted characteristics, and showed performance of 81.65%, precision 98%, and reproduction rate 71%. As a result, it was confirmed that the typical characteristics of significant performance improvement were the number of confirmed cases and days of the week, and in the case of weather information, there was no clear correlation between confirmed cases and weather information.
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항생제 사례로 살펴본 건강보험청구자료 분석 시 고려사항
김예슬,채정미,신지혜,김유정,조가영,지승연,김동숙 건강보험심사평가원 2023 HIRA RESEARCH Vol.3 No.2
The Health Insurance Review & Assessment Service (HIRA) collects and stores medical information of all citizens and medical institutions, including pharmaceutical claims data. Claims data including consultation notes and information about the medical institution are submitted to the HIRA by medical providers. Claims data have potential to offer a high quality for research, but various factors must be strongly considered in its analysis and interpretation. This study highlights various points to be considered while utilizing claims data. First, the time period between a claims review and loading of the data onto a data warehouse (DW) must be taken into account because over 99% of claims are available on DW for extraction after 5 months, according to an analysis of claims rate for pharmaceutical expenses. Second, over 50% of pharmaceutical claims data consists of reimbursable items, of which over 99% are antimicrobial medications. Accurate determination of the precise usage of nonreimbursable pharmaceuticals is limited to estimations based on the data on pharmaceutical supply declaration, which is only partially reported on claims statements as part of the comprehensive fees system. Finally, complete data on the use of pharmaceuticals during inpatient admission is unavailable. Due to the lack of requirement to input the administration time or to differentiate prescriptions on the day of discharge, it is difficult to accurately calculate the used amounts. These factors must be considered prior to analysis and interpretation of claims data from the HIRA. Hence, to ensure a higher research quality, we recommend considering these factors when conducting a claims data-based research.
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