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

Producción científica: Capítulo en Libro/Informe › Contribución a la conferencia

Resumen

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.

Idioma original	Inglés estadounidense
Título de la publicación alojada	VI Latin American Congress on Biomedical Engineering, CLAIB 2014
Editores	Ariel Braidot, Alejandro Hadad
Editorial	Springer
Páginas	761-764
Número de páginas	4
ISBN (versión digital)	9783319131160
DOI	https://doi.org/10.1007/978-3-319-13117-7_194
Estado	Publicada - 2015
Publicado de forma externa	Sí
Evento	6th Latin American Congress on Biomedical Engineering, CLAIB 2014 - Paraná, Argentina Duración: oct. 29 2014 → oct. 31 2014

Serie de la publicación

Nombre	IFMBE Proceedings
Volumen	49
ISSN (versión impresa)	1680-0737

Conferencia

Conferencia	6th Latin American Congress on Biomedical Engineering, CLAIB 2014
País/Territorio	Argentina
Ciudad	Paraná
Período	10/29/14 → 10/31/14

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

ODS 3: Salud y bienestar

Áreas temáticas de ASJC Scopus

Bioingeniería
Ingeniería biomédica

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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",

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}

Orjuela-Cañón, AD & Gómez-Cajas, DF 2015, Thoracic surgery patients data analysis using SOM neural networks. En 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

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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.

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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

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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

Resumen

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Conferencia

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