Health Mining: a new data fusion and integration paradigm

Alessandro Di Stefano; Aurelio La Corte; Marialisa Scatá

Health Mining: a new data fusion and integration paradigm

Alessandro Di Stefano, Aurelio La Corte, Marialisa Scatá

Department of Computing & Games

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

56 Downloads (Pure)

Abstract

One of the future challenges of information and communication technology (ICT) is particularly targeted at public health and clinical interventions management. The aim is to make smart the management and the processes that characterize this context to optimize the procedures and provide technological support. In this paper we propose the starting point of a new approach through architecture to introduce methodologies for fusing, integrating and drawing inference from a plurality of clinical, social and Internet of Things data, extracted from different sources. Health Mining is the central node of the proposed architecture and serves as a knowledge base for bio-inspired systems to support and perform the diagnostic process; this approach is better than the one applied with a plurality of disgregated data.

Original language	English
Title of host publication	Proceedings of CIBB 2014
Editors	Roberto Tagliaferri
Publication status	Published - 2014
Event	11th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB) - Computer Laboratory, University Of Cambridge, Cambridge, United Kingdom Duration: 26 Jun 2014 → 28 Jun 2014

Conference

Conference	11th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB)
Abbreviated title	CIBB 2014
Country/Territory	United Kingdom
City	Cambridge
Period	26/06/14 → 28/06/14

Access to Document

cibb2014_finalAccepted author manuscript, 141 KBLicence: Unspecified

Cite this

@inproceedings{3672371b074b4b16a866657fc3473656,

title = "Health Mining: a new data fusion and integration paradigm",

abstract = "One of the future challenges of information and communication technology (ICT) is particularly targeted at public health and clinical interventions management. The aim is to make smart the management and the processes that characterize this context to optimize the procedures and provide technological support. In this paper we propose the starting point of a new approach through architecture to introduce methodologies for fusing, integrating and drawing inference from a plurality of clinical, social and Internet of Things data, extracted from different sources. Health Mining is the central node of the proposed architecture and serves as a knowledge base for bio-inspired systems to support and perform the diagnostic process; this approach is better than the one applied with a plurality of disgregated data.",

author = "{Di Stefano}, Alessandro and {La Corte}, Aurelio and Marialisa Scat{\'a}",

year = "2014",

language = "English",

editor = "Tagliaferri, {Roberto }",

booktitle = "Proceedings of CIBB 2014",

note = "11th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB), CIBB 2014 ; Conference date: 26-06-2014 Through 28-06-2014",

}

TY - GEN

T1 - Health Mining: a new data fusion and integration paradigm

AU - Di Stefano, Alessandro

AU - La Corte, Aurelio

AU - Scatá, Marialisa

PY - 2014

Y1 - 2014

N2 - One of the future challenges of information and communication technology (ICT) is particularly targeted at public health and clinical interventions management. The aim is to make smart the management and the processes that characterize this context to optimize the procedures and provide technological support. In this paper we propose the starting point of a new approach through architecture to introduce methodologies for fusing, integrating and drawing inference from a plurality of clinical, social and Internet of Things data, extracted from different sources. Health Mining is the central node of the proposed architecture and serves as a knowledge base for bio-inspired systems to support and perform the diagnostic process; this approach is better than the one applied with a plurality of disgregated data.

AB - One of the future challenges of information and communication technology (ICT) is particularly targeted at public health and clinical interventions management. The aim is to make smart the management and the processes that characterize this context to optimize the procedures and provide technological support. In this paper we propose the starting point of a new approach through architecture to introduce methodologies for fusing, integrating and drawing inference from a plurality of clinical, social and Internet of Things data, extracted from different sources. Health Mining is the central node of the proposed architecture and serves as a knowledge base for bio-inspired systems to support and perform the diagnostic process; this approach is better than the one applied with a plurality of disgregated data.

M3 - Conference contribution

BT - Proceedings of CIBB 2014

A2 - Tagliaferri, Roberto

T2 - 11th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB)

Y2 - 26 June 2014 through 28 June 2014

ER -

Health Mining: a new data fusion and integration paradigm

Abstract

Conference

Access to Document

Fingerprint

Cite this