Mining discriminative patterns to predict health status for cardiopulmonary patients

Qian Cheng, Jingbo Shang, Joshua Juen, Jiawei Han, Bruce Schatz

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

Abstract

Smartphones are ubiquitous now, but it is still unclear what physiological functions they can monitor at clinical quality. Pulmonary function is a standard measure of health status for cardiopulmonary patients. We have shown that predictive models can accurately classify cardiopulmonary conditions from healthy status, as well as different severity levels within cardiopulmonary disease, the GOLD stages. Here we propose several universal models to monitor cardiopulmonary conditions, including DPClass, a novel learning approach we designed. We carefully prepare motion dataset covering status from GOLD 0 (healthy), GOLD 1(mild), GOLD 2(moderate), all the way to GOLD 3 (severe). Sixty-six subjects participate in this study. After de-identification, their walking data are applied to train the predictive models. The RBF-SVM model yields the highest accuracy while the DPClass model provides better interpretation of the model mechanisms. We not only provide promising solutions to monitor health status by simply carrying a smartphone, but also demonstrate how demographics influences predictive models of cardiopulmonary disease.

Original languageEnglish (US)
Title of host publicationACM-BCB 2016 - 7th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics
PublisherAssociation for Computing Machinery, Inc
Pages41-49
Number of pages9
ISBN (Electronic)9781450342254
DOIs
StatePublished - Oct 2 2016
Event7th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, ACM-BCB 2016 - Seattle, United States
Duration: Oct 2 2016Oct 5 2016

Publication series

NameACM-BCB 2016 - 7th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics

Other

Other7th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, ACM-BCB 2016
CountryUnited States
CitySeattle
Period10/2/1610/5/16

Keywords

  • Chronic disease assessment
  • Discriminative pattern mining
  • Free-living health monitoring

ASJC Scopus subject areas

  • Software
  • Health Informatics
  • Biomedical Engineering
  • Computer Science Applications

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  • Cite this

    Cheng, Q., Shang, J., Juen, J., Han, J., & Schatz, B. (2016). Mining discriminative patterns to predict health status for cardiopulmonary patients. In ACM-BCB 2016 - 7th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (pp. 41-49). (ACM-BCB 2016 - 7th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics). Association for Computing Machinery, Inc. https://doi.org/10.1145/2975167.2975171