Towards knowledge-based integration of personal health record data from sensors and patient observations

J. Puentes; Jaakko Lähteenmäki

Towards knowledge-based integration of personal health record data from sensors and patient observations

J. Puentes
, Jaakko Lähteenmäki

Institut Mines-Télécom (IMT)

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

1 Citation (Scopus)

Abstract

Personal Health Records (PHR) containing physiological data collected by multiple sensors are being increasingly used for wellness monitoring or disease management. These abundant complementary raw data could be nevertheless disregarded given the challenges to understand and process it. We propose a knowledge-based integration model of PHR data from sensors and personal observations, intended to facilitate decision support in scenarios of cardiovascular disease monitoring. The model relates knowledge at three data integration layers: elements identification, relations assessment, and refinement. Details on specific elements of each layer are provided, along with a discussion of use and implementation guidelines

Original language	English
Title of host publication	Proceedings of the International Conference on Health Informatics, HEALTHINF 2011
Publisher	SciTePress
Pages	280-285
ISBN (Print)	978-9-8984-2534-8
Publication status	Published - 2011
MoE publication type	A4 Article in a conference publication
Event	International Conference on Health Informatics, HEALTHINF 2011 - Rome, Italy Duration: 26 Jan 2011 → 29 Jan 2011

Conference

Conference	International Conference on Health Informatics, HEALTHINF 2011
Abbreviated title	HEALTHINF 2011
Country/Territory	Italy
City	Rome
Period	26/01/11 → 29/01/11

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

Body monitoring
data understanding
heterogeneous data integration
knowledge model
personal health record
physiological sensors

Cite this

@inproceedings{14322c5601204366af5abb0779645912,

title = "Towards knowledge-based integration of personal health record data from sensors and patient observations",

abstract = "Personal Health Records (PHR) containing physiological data collected by multiple sensors are being increasingly used for wellness monitoring or disease management. These abundant complementary raw data could be nevertheless disregarded given the challenges to understand and process it. We propose a knowledge-based integration model of PHR data from sensors and personal observations, intended to facilitate decision support in scenarios of cardiovascular disease monitoring. The model relates knowledge at three data integration layers: elements identification, relations assessment, and refinement. Details on specific elements of each layer are provided, along with a discussion of use and implementation guidelines",

keywords = "Body monitoring, data understanding, heterogeneous data integration, knowledge model, personal health record, physiological sensors",

author = "J. Puentes and Jaakko L{\"a}hteenm{\"a}ki",

year = "2011",

language = "English",

isbn = "978-9-8984-2534-8",

pages = "280--285",

booktitle = "Proceedings of the International Conference on Health Informatics, HEALTHINF 2011",

publisher = "SciTePress",

address = "Portugal",

note = "International Conference on Health Informatics, HEALTHINF 2011, HEALTHINF 2011 ; Conference date: 26-01-2011 Through 29-01-2011",

}

Towards knowledge-based integration of personal health record data from sensors and patient observations. / Puentes, J.; Lähteenmäki, Jaakko.
Proceedings of the International Conference on Health Informatics, HEALTHINF 2011. SciTePress, 2011. p. 280-285.

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

TY - GEN

T1 - Towards knowledge-based integration of personal health record data from sensors and patient observations

AU - Puentes, J.

AU - Lähteenmäki, Jaakko

PY - 2011

Y1 - 2011

N2 - Personal Health Records (PHR) containing physiological data collected by multiple sensors are being increasingly used for wellness monitoring or disease management. These abundant complementary raw data could be nevertheless disregarded given the challenges to understand and process it. We propose a knowledge-based integration model of PHR data from sensors and personal observations, intended to facilitate decision support in scenarios of cardiovascular disease monitoring. The model relates knowledge at three data integration layers: elements identification, relations assessment, and refinement. Details on specific elements of each layer are provided, along with a discussion of use and implementation guidelines

AB - Personal Health Records (PHR) containing physiological data collected by multiple sensors are being increasingly used for wellness monitoring or disease management. These abundant complementary raw data could be nevertheless disregarded given the challenges to understand and process it. We propose a knowledge-based integration model of PHR data from sensors and personal observations, intended to facilitate decision support in scenarios of cardiovascular disease monitoring. The model relates knowledge at three data integration layers: elements identification, relations assessment, and refinement. Details on specific elements of each layer are provided, along with a discussion of use and implementation guidelines

KW - Body monitoring

KW - data understanding

KW - heterogeneous data integration

KW - knowledge model

KW - personal health record

KW - physiological sensors

M3 - Conference article in proceedings

SN - 978-9-8984-2534-8

SP - 280

EP - 285

BT - Proceedings of the International Conference on Health Informatics, HEALTHINF 2011

PB - SciTePress

T2 - International Conference on Health Informatics, HEALTHINF 2011

Y2 - 26 January 2011 through 29 January 2011

ER -

Towards knowledge-based integration of personal health record data from sensors and patient observations

Abstract

Conference

UN SDGs

Keywords

Fingerprint

Cite this