TY - GEN
T1 - Mining discriminative patterns to predict health status for cardiopulmonary patients
AU - Cheng, Qian
AU - Shang, Jingbo
AU - Juen, Joshua
AU - Han, Jiawei
AU - Schatz, Bruce
N1 - Publisher Copyright:
© 2016 ACM.
PY - 2016/10/2
Y1 - 2016/10/2
N2 - 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.
AB - 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.
KW - Chronic disease assessment
KW - Discriminative pattern mining
KW - Free-living health monitoring
UR - http://www.scopus.com/inward/record.url?scp=85009826594&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85009826594&partnerID=8YFLogxK
U2 - 10.1145/2975167.2975171
DO - 10.1145/2975167.2975171
M3 - Conference contribution
AN - SCOPUS:85009826594
T3 - ACM-BCB 2016 - 7th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics
SP - 41
EP - 49
BT - ACM-BCB 2016 - 7th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics
PB - Association for Computing Machinery, Inc
T2 - 7th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, ACM-BCB 2016
Y2 - 2 October 2016 through 5 October 2016
ER -