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Yang Yuseon,김혜정,황지은 대한의학회 2020 Journal of Korean medical science Vol.35 No.49
With the rapid spread of coronavirus disease 2019 (COVID-19), a particularly sharp increase in the number of confirmed cases in Daegu and Gyeongbuk regions at the end of February, Korea faced an unprecedented shortage of medical resources, including hospital beds. To cope with this shortage, the government introduced a severity scoring system for patients with COVID-19 and designed a new type of quarantine facility for treating and isolating patients with mild symptoms out of the hospital, namely, the Residential Treatment Center (RTC). A patient with mild symptoms was immediately isolated in the RTC and continuously monitored to detect changes in symptoms. If the symptoms aggravate, the patient was transferred to a hospital. RTCs were designed by creating a quarantine environment in existing lodging facilities capable of accommodating > 100 individuals. The facilities were entirely divided into a clean zone (working area) and contaminated zone (patient zone), separating the space, air, and movement routes, and the staff wore level D personal protective equipment (PPE) in the contaminated zone. The staffs consisted of medical personnel, police officers, soldiers, and operation personnel, and worked in two or three shifts per day. Their duty was mainly to monitor the health conditions of quarantined patients, provide accommodations, and regularly collect specimens to determine if they can be released. For the past two months, RTCs secured approximately 4,000 isolation rooms and treated approximately 3,000 patients with mild symptoms and operated stably without additional spread of the disease in and out of the centers. Based on these experience, we would like to suggest the utilization of RTCs as strategic quarantine facilities in pandemic situations.
ChatGPT를 활용한 텍스트 데이터에서 키워드 추출: 수동방식과의 유사도 비교를 중심으로
임태형 ( Taehyeong Lim ),양은별 ( Eunbyul Yang ),기수현 ( Suhyun Ki ),김국현 ( Kukhyeon Kim ),정유선 ( Yuseon Jeong ),이선옥 ( Sunok Lee ),류지헌 ( Jeeheon Ryu ) 한국교육공학회 2023 교육공학연구 Vol.39 No.0
This study aimed to assess the similarity between human and ChatGPT methods in keyword extraction for text mining, focusing on short-length text data, specifically course titles in AI convergence education. A total of 442 course names from AI convergence education academic programs were previously manually analyzed and extracted in a prior study. The data included results of up to three keywords from each course name, extracted by three field experts. Utilizing the ChatGPT API, three keywords were extracted from these 442 course titles using both GPT-3.5 and GPT-4.0 models. Similarity among the manual method, GPT-3.5, and GPT-4.0 was gauged using the Jaccard Coefficient, considering coefficients greater than 0.50 indicative of similarity. In a three-way comparison, a similarity of approximately 0.69 was observed between GPT-3.5 and GPT-4.0, approximately 0.50 between the manual method and GPT-3.5, and approximately 0.48 between the manual method and GPT-4.0. Various characteristics in keyword extraction results between the manual and ChatGPT methods were reported. The significance of this study lies in revealing that, although the ChatGPT approach may not yet replace human manual methods for keyword extraction from short Korean texts, it demonstrates considerable potential.