Predictive and exposome analytics: A case study of asthma exacerbation management

Shayma Alkobaisi, Wan D. Bae, Matthew Horak, Sada Narayanappa, Jongwon Lee, Eman Abukhousa, Choon Sik Park, Da Jung Bae

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

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 and prevention by providing meaningful and understandable feedback on individuals' collected data and minimizing their exposures to health risks. The exposome paradigm focuses on the simultaneous monitoring of mobility behaviors and measurement of environmental conditions to capture their impact on human health. In this paper, we introduce the concept of exposome analytics that compliments predictive analytics to develop an effective health monitoring and management system. We present the current analytical developments including our ongoing project to manage risks of asthma exacerbations as a case study. Our proposed approach uses a novel exposome assessment paradigm that utilizes the spatiooral properties of the data in the model training process and hence results in improving the accuracy of asthma prediction. The quality of the proposed approach is extensively evaluated using real patients and environmental datasets.

Original languageEnglish
Pages (from-to)527-552
Number of pages26
JournalJournal of Ambient Intelligence and Smart Environments
Volume11
Issue number6
DOIs
Publication statusPublished - 2019
Externally publishedYes

Keywords

  • Exposome
  • asthma risk management
  • classification
  • individual-level health analytics
  • logistic regression
  • predictive health analytics
  • quantile regression

ASJC Scopus subject areas

  • Software

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