[논문]토픽모델링과 네트워크분석을 활용한 헬스케어 분야의 핵심기술과 기술융합 분석 연구: 특허정보를 중심으로

김은정; 최희진

doi:10.6109/jkiice.2022.26.5.763

토픽모델링과 네트워크분석을 활용한 헬스케어 분야의 핵심기술과 기술융합 분석 연구: 특허정보를 중심으로
Analyzing Core Tehnology and Technological Convergence in Healthcare Using Topic Modeling and Network Analysis: Focus on Patent Information 원문보기

한국정보통신학회논문지 = Journal of the Korea Institute of Information and Communication Engineering, v.26 no.5, 2022년, pp.763 - 778

김은정 (Electronics and Telecommunications Research Institute) , 최희진 (Electronics and Telecommunications Research Institute)

초록
AI-Helper

본 연구는 융합산업으로 각광받고 있는 헬스케어 분야를 중심으로 기술융합을 이루는 요소기술과 핵심기술을 파악하여 기술융합 현상을 분석하고자 하였으며, 이를 위해 2011년부터 2020년까지의 국내 특허 중 헬스케어와 관련 있는 특허 총 376개를 수집하여 토픽모델링과 네트워크 분석을 수행하였다. 첫째, 토픽모델링 분석 결과 "데이터수집·분석", "생체신호측정", "건강관리", "디지털정보 수집 및 전송", "진단·치료", "측정 진단장치" 총 6개의 주요 토픽이 도출되었다. 둘째, 앞서 분석한 토픽별로 네트워크 분석을 수행하여 기술간 연결망 구조를 파악한 후 기술융합 특성을 확인하고, 중심성 지표를 통해 핵심기술을 도출하였다. 본 연구의 핵심인 국내 헬스케어의 핵심·요소기술동향 및 기술융합도 분석결과는, 기업의 신규 가치창출을 위한 제품·서비스개발 방향성을 수립하거나, 학계 및 정부의 헬스케어 산업을 육성·지원하기 위한 전략 및 정책적인 방향성을 수립하는데 기초자료로 활용될 수 있을 것이다.

Abstract ▼ AI-Helper

In this study we aim to identify the core technologies that play central roles along with the peripheral technologies that contribute to the technology convergence in digital healthcare. A total of 376 korean-patents related to healthcare were gathered from 2011 to 2020, and a topic modeling technique and a network analysis were conducted on the collected data. Six major topics were derived through the topic modeling procedure which are "data collection", "signal measurement", "health management", "data transmission", "diagnostic treatment", and "measurement device". Each of the six topics were analyzed to depict relations among technologies, specify the convergence characteristics, and identify the core-technology through centrality analysis. The study illustrates the present status of digital healthcare technology development and the technological convergence in South Korea and is anticipated to help establish policies to foster healthcare industry.

주제어

표/그림 (20)

그림 Fig. 1 Research Procedure
그림 Fig. 2 Latent Dirichlet Allocation Model ⍺ _k : prior weight of topic k in a document θ _d : distribution of topics in document d β _k : distribution of words in topic k η _w : prior weight of word w in a topic Z _d,w : identity of topic of word w in document d W _d,w : identity of word w in document d M : number of documents N _d : number of words in document d k : number of topics
표 Table. 1 Result of Topic modeling(LDA)
그림 Fig. 3 Topic 1 Network Analysis
표 Table. 2 Topic 1 Centrality Analysis
그림 Fig. 4 Topic 2 Network Analysis
표 Table. 3 Topic 2 Centrality Analysis
그림 Fig. 5 Topic 3 Network Analysis
표 Table. 4 Topic 3 Centrality Analysis
그림 Fig. 6 Topic 4 Network Analysis
표 Table. 5 Topic 4 Centrality Analysis
그림 Fig. 7 Topic 5 Network Analysis
표 Table. 6 Topic 5 Centrality Analysis
그림 Fig. 8 Topic 6 Network Analysis
표 Table. 7 Topic 6 Centrality Analysis
그림 Fig. 9 Network Analysis of all patent
표 Table. 8 Results of centrality analysis of all patents
표 Table. 9 Results of centrality analysis of all patents
표 Table. 10 Results of centrality analysis of patents
그림 Fig. 10 A framework for healthcare technology based on analysis results

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