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Analysis of the Pathogeneses of Multiple Organ Dysfunction Syndrome Using Data Mashups and Big Data Techniques

첫 페이지 보기
  • 발행기관
    대한산업경영학회 바로가기
  • 간행물
    International Journal of Intelligent Technologies and Innovative Practices 바로가기
  • 통권
    Vol. 1 No. 2 (2026.04)바로가기
  • 페이지
    pp.21-39
  • 저자
    Mi Ri Kim, Hyong Jung Kim, Jinhwa Kim
  • 언어
    영어(ENG)
  • URL
    https://www.earticle.net/Article/A484935

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원문정보

초록

영어
This study investigates the pathogeneses and interactions among diseases associated with multiple organ dysfunction syndrome (MODS). The research applied a three-step analytical framework using data mashups and big data techniques. First, association rule analysis was conducted using hospital mortality data from general hospitals. Second, text-mining techniques were applied to medical information collected through web crawling from the PLM network. Third, social media data from Twitter and blogs were analyzed to identify hidden disease relationships. The study identified significant associations between pneumonia, sepsis, respiratory insufficiency, lung cancer, and MODS. Results showed that complications, infections, viruses, and inflammation play major roles in disease progression. Pneumonia was strongly linked to respiratory insufficiency and MODS through reduced immune function and lung damage. Sepsis and septic shock were also found to contribute significantly to organ failure and mortality. The research demonstrated that integrating structured and unstructured medical data can reveal meaningful pathogenic pathways. The proposed framework provides a quantitative method for mapping disease interactions and improving clinical understanding. This study contributes to future medical big data research by supporting predictive analysis and clinical decision-making.

목차

Abstract
1. INTRODUCTION
2. THEORETICAL BACKGROUND
2.1. Text Mining Techniques
2.2. Association Rule Mining
3. RESEARCH DESIGN
3.1. Research Process and Model
3.2. Data Collection
4. ANALYSIS AND RESULTS
4.1. Analysis of Data from Hospital Patients
4.2. Analysis of Data from the PatientsLikeMe (PLM) Network
4.3. Analysis of Twitter and Blog
4.4. Analysis of Results by Data Mashup
5. CONCLUSION
REFERENCES

키워드

Multiple Organ Dysfunction Syndrome (MODS) Big Data Analytics Text Mining Data Mashup Disease Pathogenesis Association Rule Analysis

저자

  • Mi Ri Kim [ Sogang Business School, Seoul, South Korea ]
  • Hyong Jung Kim [ Korea Productivity Center, Seoul, South Korea ]
  • Jinhwa Kim [ Sogang Business School, Seoul, South Korea ] Corresponding Author

참고문헌

자료제공 : 네이버학술정보

간행물 정보

발행기관

  • 발행기관명
    대한산업경영학회 [Dae Han Society of Industrial Management]
  • 설립연도
    2003
  • 분야
    복합학>과학기술학
  • 소개
    본 학회는 산업체·학계·연구소 등의 회원 상호간에 정보교환 및 지원을 통하여 산업경영에 관한 학문발전을 도모하고 산학에 관한 긴밀한 네트워크를 형성하여 기업의 경쟁력을 강화시키는데 그 설립 목적을 두고 있다.

간행물

  • 간행물명
    International Journal of Intelligent Technologies and Innovative Practices
  • 간기
    계간
  • eISSN
    3092-412X
  • 수록기간
    2026~2026
  • 십진분류
    KDC 323 DDC 338

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