TY - GEN
T1 - A two-step approach to predictive modeling of individual-based environmental health risks
AU - Bae, Wan D.
AU - Horak, Matthew
AU - Narayanappa, Sada
AU - Alkobaisi, Shayma
AU - Kim, Sehjeong
AU - Park, Choon Sik
AU - Bae, Da Jeong
N1 - Funding Information:
The authors would like to thank the doctors and medical staff in Division of Allergy and Respiratory Medicine at Soonchunhyang University Bucheon Hospital for providing the data. This material is based upon works supported in part by Hanyang University under Grant No 2016473 and in part by the Information and Communication Technology Fund of the United Arab Emirates under Grant No 21T42. The study was also funded by Korean Ministry of Environment under Gran No HI16-00136-0603-0.
Publisher Copyright:
© 2019 Association for Computing Machinery.
PY - 2019
Y1 - 2019
N2 - The emerging predictive health analytics provides great promise in reducing costs and improving health outcomes. However, most predictive models do not capture environmental exposures that impact health risk patterns in several chronic diseases such as asthma. This gap prompted the development of the exposome paradigm to improve health intervention by providing meaningful and understandable feedback on collected data. In this paper, we investigate a number of commonly used classification models applied in predicting health risks of asthma, given patients and environmental exposure datasets. We discuss the limitations of these existing models and propose a two-step approach of logistic and quantile regression, which provides a meaningful and comprehensive feedback for patients. The proposed approach uses a novel exposome assessment paradigm that utilizes the spatio-temporal properties of the data in the model training process and hence results in improving the accuracy of prediction. The quality of the proposed approach is extensively evaluated using real patients and environmental datasets.
AB - The emerging predictive health analytics provides great promise in reducing costs and improving health outcomes. However, most predictive models do not capture environmental exposures that impact health risk patterns in several chronic diseases such as asthma. This gap prompted the development of the exposome paradigm to improve health intervention by providing meaningful and understandable feedback on collected data. In this paper, we investigate a number of commonly used classification models applied in predicting health risks of asthma, given patients and environmental exposure datasets. We discuss the limitations of these existing models and propose a two-step approach of logistic and quantile regression, which provides a meaningful and comprehensive feedback for patients. The proposed approach uses a novel exposome assessment paradigm that utilizes the spatio-temporal properties of the data in the model training process and hence results in improving the accuracy of prediction. The quality of the proposed approach is extensively evaluated using real patients and environmental datasets.
KW - Asthma risk management
KW - Classification
KW - Exposome
KW - Individual-level health analytics
KW - Logistic regression
KW - Predictive health analytics
KW - Quantile regression
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U2 - 10.1145/3297280.3297350
DO - 10.1145/3297280.3297350
M3 - Conference contribution
AN - SCOPUS:85065655781
SN - 9781450359337
T3 - Proceedings of the ACM Symposium on Applied Computing
SP - 729
EP - 738
BT - Proceedings of the ACM Symposium on Applied Computing
PB - Association for Computing Machinery
T2 - 34th Annual ACM Symposium on Applied Computing, SAC 2019
Y2 - 8 April 2019 through 12 April 2019
ER -