Predicting health care risk with big data drawn from clinical physiological parameters

Honghao Wei*, Yang Yang, Huan Chen, Bin Xu, Jian Li, Miao Jiang, Aiping LYU

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

1 Citation (Scopus)


Fatty liver often afflicts patients seriously and jeopardizes the health of human race with high possibility of deteriorating into cirrhosis and liver cancer, which motivates researchers to detect causes and potential influential factors. In this paper, we study the problem of detecting the potential influential factors in workplaces and their contributions to the morbidity. To this end, gender and age, retirement status and department information are chosen as three potential influential factors in workplaces. By analyzing those factors with demographics, Propensity Score Matching and classic classifier models, we mine the relationship between the workplace factors and morbidity. This finding indicates a new domain of discussing the causes of fatty liver which originally focuses on daily diets and lifestyles.

Original languageEnglish
Title of host publicationSocial Media Processing - 3rd National Conference, SMP 2014, Proceedings
EditorsJie Tang, Ting Liu, Heyan Huang, Hua-Ping Zhang
PublisherSpringer Verlag
Number of pages11
ISBN (Electronic)9783662455579
Publication statusPublished - 2014
Event3rd National Conference on Social Media Processing, SMP 2014 - Beijing, China
Duration: 1 Nov 20142 Nov 2014

Publication series

NameCommunications in Computer and Information Science
ISSN (Print)1865-0929


Conference3rd National Conference on Social Media Processing, SMP 2014

Scopus Subject Areas

  • Computer Science(all)
  • Mathematics(all)

User-Defined Keywords

  • Department information
  • Fatty liver
  • Gender and age
  • Retirement status


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