Introduction to part III: Markers to mHealth predictors

James M. Rehg; Susan A. Murphy; Santosh Kumar

doi:10.1007/978-3-319-51394-2_17

Introduction to part III: Markers to mHealth predictors

James M. Rehg
, Susan A. Murphy
, Santosh Kumar

Research output: Chapter in Book/Report/Conference proceeding › Chapter

Abstract

Given the ability to extract physiological and behavioral markers from continuous streams of sensor data, a key challenge is to convert the resulting marker sequences into predictions of risk for adverse outcomes, that can be used to inform interventions. The four articles in this part cover visualization, models for temporal data, and a case study on predicting high-stress events.

Original language	English (US)
Title of host publication	Mobile Health
Subtitle of host publication	Sensors, Analytic Methods, and Applications
Editors	James M Rehg, Susan A Murphy, Santosh Kumar
Publisher	Springer
Pages	345-348
Number of pages	4
ISBN (Electronic)	9783319513942
ISBN (Print)	9783319513935
DOIs	https://doi.org/10.1007/978-3-319-51394-2_17
State	Published - Jul 12 2017
Externally published	Yes

ASJC Scopus subject areas

General Medicine
General Computer Science

Online availability

10.1007/978-3-319-51394-2_17

Library availability

Discover UIUC Full Text

Cite this

@inbook{b5edb436efb648c7826bad06fcde8da8,

title = "Introduction to part III: Markers to mHealth predictors",

abstract = "Given the ability to extract physiological and behavioral markers from continuous streams of sensor data, a key challenge is to convert the resulting marker sequences into predictions of risk for adverse outcomes, that can be used to inform interventions. The four articles in this part cover visualization, models for temporal data, and a case study on predicting high-stress events.",

author = "Rehg, \{James M.\} and Murphy, \{Susan A.\} and Santosh Kumar",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.",

year = "2017",

month = jul,

day = "12",

doi = "10.1007/978-3-319-51394-2\_17",

language = "English (US)",

isbn = "9783319513935",

pages = "345--348",

editor = "Rehg, \{James M\} and Murphy, \{Susan A\} and Santosh Kumar",

booktitle = "Mobile Health",

publisher = "Springer",

address = "Germany",

}

TY - CHAP

T1 - Introduction to part III

T2 - Markers to mHealth predictors

AU - Rehg, James M.

AU - Murphy, Susan A.

AU - Kumar, Santosh

N1 - Publisher Copyright: © Springer International Publishing AG 2017.

PY - 2017/7/12

Y1 - 2017/7/12

N2 - Given the ability to extract physiological and behavioral markers from continuous streams of sensor data, a key challenge is to convert the resulting marker sequences into predictions of risk for adverse outcomes, that can be used to inform interventions. The four articles in this part cover visualization, models for temporal data, and a case study on predicting high-stress events.

AB - Given the ability to extract physiological and behavioral markers from continuous streams of sensor data, a key challenge is to convert the resulting marker sequences into predictions of risk for adverse outcomes, that can be used to inform interventions. The four articles in this part cover visualization, models for temporal data, and a case study on predicting high-stress events.

UR - https://www.scopus.com/pages/publications/85055379461

UR - https://www.scopus.com/pages/publications/85055379461#tab=citedBy

U2 - 10.1007/978-3-319-51394-2_17

DO - 10.1007/978-3-319-51394-2_17

M3 - Chapter

AN - SCOPUS:85055379461

SN - 9783319513935

SP - 345

EP - 348

BT - Mobile Health

A2 - Rehg, James M

A2 - Murphy, Susan A

A2 - Kumar, Santosh

PB - Springer

ER -

Introduction to part III: Markers to mHealth predictors

Abstract

ASJC Scopus subject areas

Online availability

Library availability

Related links

Fingerprint

Mobile health: Sensors, analytic methods, and applications

Cite this