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

doi:10.1007/978-3-662-45558-6

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

School of Chinese Medicine

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

1 Citation (Scopus)

Abstract

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 language	English
Title of host publication	Social Media Processing - 3rd National Conference, SMP 2014, Proceedings
Editors	Jie Tang, Ting Liu, Heyan Huang, Hua-Ping Zhang
Publisher	Springer Verlag
Pages	88-98
Number of pages	11
ISBN (Electronic)	9783662455579
DOIs	https://doi.org/10.1007/978-3-662-45558-6
Publication status	Published - 2014
Event	3rd National Conference on Social Media Processing, SMP 2014 - Beijing, China Duration: 1 Nov 2014 → 2 Nov 2014

Publication series

Name	Communications in Computer and Information Science
Volume	489
ISSN (Print)	1865-0929

Conference

Conference	3rd National Conference on Social Media Processing, SMP 2014
Country/Territory	China
City	Beijing
Period	1/11/14 → 2/11/14

Scopus Subject Areas

General Computer Science
General Mathematics

User-Defined Keywords

Department information
Fatty liver
Gender and age
Retirement status

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-662-45558-6

Cite this

Wei, H., Yang, Y., Chen, H., Xu, B., Li, J., Jiang, M., & LYU, A. (2014). Predicting health care risk with big data drawn from clinical physiological parameters. In J. Tang, T. Liu, H. Huang, & H.-P. Zhang (Eds.), Social Media Processing - 3rd National Conference, SMP 2014, Proceedings (pp. 88-98). (Communications in Computer and Information Science; Vol. 489). Springer Verlag. https://doi.org/10.1007/978-3-662-45558-6

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title = "Predicting health care risk with big data drawn from clinical physiological parameters",

abstract = "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.",

keywords = "Department information, Fatty liver, Gender and age, Retirement status",

author = "Honghao Wei and Yang Yang and Huan Chen and Bin Xu and Jian Li and Miao Jiang and Aiping LYU",

note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2014.; 3rd National Conference on Social Media Processing, SMP 2014 ; Conference date: 01-11-2014 Through 02-11-2014",

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doi = "10.1007/978-3-662-45558-6",

language = "English",

series = "Communications in Computer and Information Science",

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Wei, H, Yang, Y, Chen, H, Xu, B, Li, J, Jiang, M & LYU, A 2014, Predicting health care risk with big data drawn from clinical physiological parameters. in J Tang, T Liu, H Huang & H-P Zhang (eds), Social Media Processing - 3rd National Conference, SMP 2014, Proceedings. Communications in Computer and Information Science, vol. 489, Springer Verlag, pp. 88-98, 3rd National Conference on Social Media Processing, SMP 2014, Beijing, China, 1/11/14. https://doi.org/10.1007/978-3-662-45558-6

Predicting health care risk with big data drawn from clinical physiological parameters. / Wei, Honghao; Yang, Yang; Chen, Huan et al.
Social Media Processing - 3rd National Conference, SMP 2014, Proceedings. ed. / Jie Tang; Ting Liu; Heyan Huang; Hua-Ping Zhang. Springer Verlag, 2014. p. 88-98 (Communications in Computer and Information Science; Vol. 489).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

TY - GEN

T1 - Predicting health care risk with big data drawn from clinical physiological parameters

AU - Wei, Honghao

AU - Yang, Yang

AU - Chen, Huan

AU - Xu, Bin

AU - Li, Jian

AU - Jiang, Miao

AU - LYU, Aiping

N1 - Publisher Copyright: © Springer-Verlag Berlin Heidelberg 2014.

PY - 2014

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N2 - 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.

AB - 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.

KW - Department information

KW - Fatty liver

KW - Gender and age

KW - Retirement status

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Wei H, Yang Y, Chen H, Xu B, Li J, Jiang M et al. Predicting health care risk with big data drawn from clinical physiological parameters. In Tang J, Liu T, Huang H, Zhang HP, editors, Social Media Processing - 3rd National Conference, SMP 2014, Proceedings. Springer Verlag. 2014. p. 88-98. (Communications in Computer and Information Science). doi: 10.1007/978-3-662-45558-6

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

Abstract

Publication series

Conference

Scopus Subject Areas

User-Defined Keywords

UN SDGs

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