This study analyzed patent trends in decision support systems for worker health management to provide foundational data for future development of worker decision support systems. A total of 989 patents retrieved from the KIPRIS website, spanning from 1997 to 2025, were analyzed using Python-based Jupyter Notebook with keywords including "worker" and IPC codes (G16H/G06N/H04W). The analysis revealed four distinct periods of technological evolution: Period 1 exhibited a concentrated hub network centered on a few core technologies; Period 2 showed keyword diversification with the emergence of platform and smart technologies; Period 3 entered a convergent stage with the proliferation of AI and big data; and Period 4 demonstrated the convergence of mega-topics based on hyper-connected platforms and generative AI technologies, illustrating temporal changes across periods. Future research could verify whether shifts in network centrality correlate with actual industrial competitiveness, potentially serving as a strategic decision-making tool.
목차
Abstract 1. 서론 2. 이론적 배경 2.1 사업장 근로자 건강관리 정책 2.2 사업장 건강관리 의사결정 시스템 3. 연구방법론 3.1 텍스트 마이닝과 빈도 분석 3.2 Term Frequency-Inverse Document Frequency (TF-IDF) 분석 3.3 네트워크 및 중심성 분석 4. 분석 결과 4.1 데이터 수집 결과 4.2 데이터 전처리 및 빈도 분석 결과 4.3 TF-IDF 분석 결과 4.4 기간별 키워드 네트워크 분석 결과 5. 결론 5.1 연구 결론 5.2 연구의 의의 및 한계 References
키워드
Worker Health ManagementDecision Support SystemPatent Trend AnalysisNetwork AnalysisArtificial Intelligence
저자
이선혜 [ Seonhye Lee | Professor, Department of Nursing, Sustainable Health Research Institute, Gyeongsang National University ]
First Author
박상혁 [ Sang Hyeok Park | Professor, Department of Industrial Management, Gyeongsang National University, Jinju, Korea ]
Co-Author
김명훈 [ Myeong Hun Kim | Occupational Safety & Environment Team, Korea Aerospace Industries, Sacheon, Korea ]
Co-Author
유기섭 [ Giseob Yu | Assistant Professor, Division of Interdisciplinary Studies, Sun Moon University ]
Corresponding Author