Skip to search boxSkip to navigationSkip to main content

Lifestyle risk association aggregation

Research Output: Chapter in Book/Report/Conference proceeding Conference contribution Peer-review

Abstract

IoT application in health care provides ways to monitor and collect health related biomarkers, in particular, lifestyle related data, by recording and analyzing long-term data, to provide insight to patients' status. In order to make most use of this application, linking the collected patients' data with a disease predictive model will generate a personalized disease progression and predictions. Various risk factors have been researched extensively to find the effect on the disease. However, risk factors are fragmented all over medical literature, and often each publication reports on one or a few risk factors, a combination of several of those factors, often from different research. In this paper, we propose an approach to explore the combination of risk factors. The outcome will form a base for a complete risk prediction model that can be used for many health applications.

Publication Information

Output type

Research Output: Chapter in Book/Report/Conference proceeding Conference contribution Peer-review

Original language

English

Pages from-to (Number of pages)

Pages 223-228

Publication milestones

  • Published - 15/08/2019

Publication status

Published - 15/08/2019

Publisher

Institute of Electrical and Electronics Engineers Inc., United States

ISBN (Print)

9781728117966

External Publication IDs

  • handle.net: 10547/623864
  • Scopus: 85071688153

Host publication title

2019 Fourth International Conference on Fog and Mobile Edge Computing (FMEC)

Access to documents

Publication metrics

Metrics