Thoracic surgery patients data analysis using SOM neural networks

A. D. Orjuela-Cañón; D. F. Gómez-Cajas

doi:10.1007/978-3-319-13117-7_194

Thoracic surgery patients data analysis using SOM neural networks

A. D. Orjuela-Cañón
, D. F. Gómez-Cajas

Research output: Chapter in Book/Inform › Conference contribution

Abstract

Data from patients after thoracic surgery caused by lung cancer are analyzed by Self Organizing Maps. Models obtained after training of these neural networks develop a clustering on synaptic weights, using k-means algorithms. Nonlinear relationships were found between patients with diseases and input variables. Results show how these models are useful for extracting value information in biomedical applications.

Original language	English (US)
Title of host publication	VI Latin American Congress on Biomedical Engineering, CLAIB 2014
Editors	Ariel Braidot, Alejandro Hadad
Publisher	Springer
Pages	761-764
Number of pages	4
ISBN (Electronic)	9783319131160
DOIs	https://doi.org/10.1007/978-3-319-13117-7_194
State	Published - 2015
Externally published	Yes
Event	6th Latin American Congress on Biomedical Engineering, CLAIB 2014 - Paraná, Argentina Duration: Oct 29 2014 → Oct 31 2014

Publication series

Name	IFMBE Proceedings
Volume	49
ISSN (Print)	1680-0737

Conference

Conference	6th Latin American Congress on Biomedical Engineering, CLAIB 2014
Country/Territory	Argentina
City	Paraná
Period	10/29/14 → 10/31/14

UN SDGs

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

SDG 3 Good Health and Well-being

All Science Journal Classification (ASJC) codes

Bioengineering
Biomedical Engineering

Access to Document

10.1007/978-3-319-13117-7_194

Cite this

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title = "Thoracic surgery patients data analysis using SOM neural networks",

abstract = "Data from patients after thoracic surgery caused by lung cancer are analyzed by Self Organizing Maps. Models obtained after training of these neural networks develop a clustering on synaptic weights, using k-means algorithms. Nonlinear relationships were found between patients with diseases and input variables. Results show how these models are useful for extracting value information in biomedical applications.",

author = "Orjuela-Ca{\~n}{\'o}n, \{A. D.\} and G{\'o}mez-Cajas, \{D. F.\}",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 6th Latin American Congress on Biomedical Engineering, CLAIB 2014 ; Conference date: 29-10-2014 Through 31-10-2014",

year = "2015",

doi = "10.1007/978-3-319-13117-7\_194",

language = "English (US)",

series = "IFMBE Proceedings",

publisher = "Springer",

pages = "761--764",

editor = "Ariel Braidot and Alejandro Hadad",

booktitle = "VI Latin American Congress on Biomedical Engineering, CLAIB 2014",

address = "Germany",

}

Orjuela-Cañón, AD & Gómez-Cajas, DF 2015, Thoracic surgery patients data analysis using SOM neural networks. in A Braidot & A Hadad (eds), VI Latin American Congress on Biomedical Engineering, CLAIB 2014. IFMBE Proceedings, vol. 49, Springer, pp. 761-764, 6th Latin American Congress on Biomedical Engineering, CLAIB 2014, Paraná, Argentina, 10/29/14. https://doi.org/10.1007/978-3-319-13117-7_194

TY - GEN

T1 - Thoracic surgery patients data analysis using SOM neural networks

AU - Orjuela-Cañón, A. D.

AU - Gómez-Cajas, D. F.

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2015.

PY - 2015

Y1 - 2015

N2 - Data from patients after thoracic surgery caused by lung cancer are analyzed by Self Organizing Maps. Models obtained after training of these neural networks develop a clustering on synaptic weights, using k-means algorithms. Nonlinear relationships were found between patients with diseases and input variables. Results show how these models are useful for extracting value information in biomedical applications.

AB - Data from patients after thoracic surgery caused by lung cancer are analyzed by Self Organizing Maps. Models obtained after training of these neural networks develop a clustering on synaptic weights, using k-means algorithms. Nonlinear relationships were found between patients with diseases and input variables. Results show how these models are useful for extracting value information in biomedical applications.

UR - https://www.scopus.com/pages/publications/84925243312

UR - https://www.scopus.com/pages/publications/84925243312#tab=citedBy

U2 - 10.1007/978-3-319-13117-7_194

DO - 10.1007/978-3-319-13117-7_194

M3 - Conference contribution

AN - SCOPUS:84925243312

T3 - IFMBE Proceedings

SP - 761

EP - 764

BT - VI Latin American Congress on Biomedical Engineering, CLAIB 2014

A2 - Braidot, Ariel

A2 - Hadad, Alejandro

PB - Springer

T2 - 6th Latin American Congress on Biomedical Engineering, CLAIB 2014

Y2 - 29 October 2014 through 31 October 2014

ER -

Thoracic surgery patients data analysis using SOM neural networks

Abstract

Publication series

Conference

UN SDGs

All Science Journal Classification (ASJC) codes

Access to Document

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