A two-step approach to predictive modeling of individual-based environmental health risks

Wan D. Bae, Matthew Horak, Sada Narayanappa, Shayma Alkobaisi, Sehjeong Kim, Choon Sik Park, Da Jeong Bae

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

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the ACM Symposium on Applied Computing
PublisherAssociation for Computing Machinery
Pages729-738
Number of pages10
ISBN (Print)9781450359337
DOIs
Publication statusPublished - 2019
Event34th Annual ACM Symposium on Applied Computing, SAC 2019 - Limassol, Cyprus
Duration: Apr 8 2019Apr 12 2019

Publication series

NameProceedings of the ACM Symposium on Applied Computing
VolumePart F147772

Conference

Conference34th Annual ACM Symposium on Applied Computing, SAC 2019
Country/TerritoryCyprus
CityLimassol
Period4/8/194/12/19

Keywords

  • Asthma risk management
  • Classification
  • Exposome
  • Individual-level health analytics
  • Logistic regression
  • Predictive health analytics
  • Quantile regression

ASJC Scopus subject areas

  • Software

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