A new prognosis factor analysis based on nonhomogeneous Markov description

Takeo Shibata, Hiroshi Tanaka, Yoshihiro Tsujimoto, Kimio Yoshimura, Takashi Fukutomi, Takeshi Nanasawa, Naohito Yamaguchi

Research output: Chapter in Book/Report/Conference proceedingConference contribution


To evaluate prognosis factors, Cox's proportional hazard model has been used. But it was found that the analytical ability was not sufficient. So we propose a new evaluation method combining Markov chain model and multiple logistic regression analysis to estimate the prognosis factors. Stage II breast cancer was chosen as the subject. The data was retrospective data gathered in National Cancer Center Central Hospital. As first step, a simple Markov chain model was constructed to describe the state transition of a breast cancer. Then the multiple property of each state transition was investigated in detail. And the patients who had gotten a recurrence for the first two and a half years were discriminated as the poor prognosis group by a nonparametric test (p<0.05). And the result proved to corresponding with the clinical experience. As second step, three factors (n classification of pathological diagnosis, ductal spread, and estrogen receptor) were selected as the prognosis factors for the early death in Stage II breast cancer by a multiple logistic regression analysis. This new prognosis factor analysis could find out some scientific evidences. Especially, it was found to be remarkable efficient in proving clinically experienced observation.

Original languageEnglish
Title of host publicationMEDINFO 2001 - Proceedings of the 10th World Congress on Medical Informatics
PublisherIOS Press
Number of pages4
ISBN (Print)1586031945, 9781586031947
Publication statusPublished - 2001
Externally publishedYes
Event10th World Congress on Medical Informatics, MEDINFO 2001 - London, United Kingdom
Duration: 2005 Sept 22005 Sept 5

Publication series

NameStudies in Health Technology and Informatics
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365


Other10th World Congress on Medical Informatics, MEDINFO 2001
Country/TerritoryUnited Kingdom


  • Markov chain model
  • breast cancer
  • multiple logistic regression analysis
  • prognosis factor

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management


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