Life style related risk association mining

Emmanuel Effiok; Enjie Liu; Jon Hitchcock

doi:10.1109/IINTEC.2018.8695295

Life style related risk association mining

Emmanuel Effiok
, Enjie Liu
, Jon Hitchcock

Institute for Research in Applicable Computing (IRAC)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

IoT application in health care provides ways to monitor and collect health related biomarkers, in particular, life-style 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. It is also important to understand one's health risks in order to benefit from new research about specific diseases and plan for preventive monitoring. Risk factors for a disease are results of various medical researches. In this paper, we propose an approach for risk factor selection and mining.

Original language	English
Title of host publication	nan
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	79-84
ISBN (Print)	9781538691311
DOIs	https://doi.org/10.1109/IINTEC.2018.8695295
Publication status	Published - 25 Apr 2019
Event	International Conference on Internet of Things, Embedded Systems and Communications (IINTEC) - Hamammet Duration: 20 Dec 2018 → 21 Dec 2018

Conference

Conference	International Conference on Internet of Things, Embedded Systems and Communications (IINTEC)
City	Hamammet
Period	20/12/18 → 21/12/18
Other	International Conference on Internet of Things, Embedded Systems and Communications (IINTEC) (20/12/2018-21/12/2018, Hamammet)

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

IOT applications
risk association
risk association mining

Access to Document

10.1109/IINTEC.2018.8695295

https://ieeexplore.ieee.org/document/8695295

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@inproceedings{83d7d1862c02453da795f3259a08b43f,

title = "Life style related risk association mining",

abstract = "IoT application in health care provides ways to monitor and collect health related biomarkers, in particular, life-style 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. It is also important to understand one's health risks in order to benefit from new research about specific diseases and plan for preventive monitoring. Risk factors for a disease are results of various medical researches. In this paper, we propose an approach for risk factor selection and mining.",

keywords = "IOT applications, risk association, risk association mining",

author = "Emmanuel Effiok and Enjie Liu and Jon Hitchcock",

year = "2019",

month = apr,

day = "25",

doi = "10.1109/IINTEC.2018.8695295",

language = "English",

isbn = "9781538691311",

pages = "79--84",

booktitle = "nan",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

note = "International Conference on Internet of Things, Embedded Systems and Communications (IINTEC) ; Conference date: 20-12-2018 Through 21-12-2018",

}

TY - GEN

T1 - Life style related risk association mining

AU - Effiok, Emmanuel

AU - Liu, Enjie

AU - Hitchcock, Jon

PY - 2019/4/25

Y1 - 2019/4/25

N2 - IoT application in health care provides ways to monitor and collect health related biomarkers, in particular, life-style 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. It is also important to understand one's health risks in order to benefit from new research about specific diseases and plan for preventive monitoring. Risk factors for a disease are results of various medical researches. In this paper, we propose an approach for risk factor selection and mining.

AB - IoT application in health care provides ways to monitor and collect health related biomarkers, in particular, life-style 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. It is also important to understand one's health risks in order to benefit from new research about specific diseases and plan for preventive monitoring. Risk factors for a disease are results of various medical researches. In this paper, we propose an approach for risk factor selection and mining.

KW - IOT applications

KW - risk association

KW - risk association mining

U2 - 10.1109/IINTEC.2018.8695295

DO - 10.1109/IINTEC.2018.8695295

M3 - Conference contribution

SN - 9781538691311

SP - 79

EP - 84

BT - nan

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - International Conference on Internet of Things, Embedded Systems and Communications (IINTEC)

Y2 - 20 December 2018 through 21 December 2018

ER -

Life style related risk association mining

Abstract

Conference

UN SDGs

Keywords

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