Identification of relevant knowledge for characterizing the melanoma domain

Ruben Nicolas; Elisabet Golobardes; Albert Fornells; Susana Puig; Cristina Carrera; Josep Malvehy

doi:10.1007/978-3-540-85861-4_7

Identification of relevant knowledge for characterizing the melanoma domain

Ruben Nicolas^*
, Elisabet Golobardes
, Albert Fornells
, Susana Puig
, Cristina Carrera
, Josep Malvehy

^*Corresponding author for this work

Research output: Book chapter › Conference contribution › peer-review

2 Citations (Scopus)

Abstract

Melanoma is one of the most important cancers to study in our current social context. This kind of cancer has increased its frequency in the last few years and its mortality is around twenty percent if it is not early treated. In order to improve the early diagnosis, the problem characterization using Machine Learning (ML) is crucial to identify melanoma patterns. Therefore we need to organize the data so that we can apply ML on it. This paper presents a detailed characterization based on the most relevant knowledge in melanomas problem and how to relate them to apply Data Mining techniques to aid medical diagnosis in melanoma and improve the research in this field.

Original language	English
Title of host publication	2nd International Workshop on Practical Applications of Computational Biology and Bioinformatics (IWPACBB 2008)
Editors	Juan Corchado, Juan De Paz, Miguel Rocha, Florentino Fernandez Riverola
Pages	55-59
Number of pages	5
DOIs	https://doi.org/10.1007/978-3-540-85861-4_7
Publication status	Published - 2009

Publication series

Name	Advances in Soft Computing
Volume	49
ISSN (Print)	1615-3871
ISSN (Electronic)	1860-0794

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

Computer aided systems
Health information systems
Knowledge management and decision support systems
Melanoma domain

Access to Document

10.1007/978-3-540-85861-4_7

Cite this

Nicolas, R., Golobardes, E., Fornells, A., Puig, S., Carrera, C., & Malvehy, J. (2009). Identification of relevant knowledge for characterizing the melanoma domain. In J. Corchado, J. De Paz, M. Rocha, & F. F. Riverola (Eds.), 2nd International Workshop on Practical Applications of Computational Biology and Bioinformatics (IWPACBB 2008) (pp. 55-59). (Advances in Soft Computing; Vol. 49). https://doi.org/10.1007/978-3-540-85861-4_7

Nicolas, Ruben ; Golobardes, Elisabet ; Fornells, Albert et al. / Identification of relevant knowledge for characterizing the melanoma domain. 2nd International Workshop on Practical Applications of Computational Biology and Bioinformatics (IWPACBB 2008). editor / Juan Corchado ; Juan De Paz ; Miguel Rocha ; Florentino Fernandez Riverola. 2009. pp. 55-59 (Advances in Soft Computing).

@inproceedings{d3b8394a98224e68b22205f23386e112,

title = "Identification of relevant knowledge for characterizing the melanoma domain",

abstract = "Melanoma is one of the most important cancers to study in our current social context. This kind of cancer has increased its frequency in the last few years and its mortality is around twenty percent if it is not early treated. In order to improve the early diagnosis, the problem characterization using Machine Learning (ML) is crucial to identify melanoma patterns. Therefore we need to organize the data so that we can apply ML on it. This paper presents a detailed characterization based on the most relevant knowledge in melanomas problem and how to relate them to apply Data Mining techniques to aid medical diagnosis in melanoma and improve the research in this field.",

keywords = "Computer aided systems, Health information systems, Knowledge management and decision support systems, Melanoma domain",

author = "Ruben Nicolas and Elisabet Golobardes and Albert Fornells and Susana Puig and Cristina Carrera and Josep Malvehy",

year = "2009",

doi = "10.1007/978-3-540-85861-4\_7",

language = "English",

isbn = "9783540858607",

series = "Advances in Soft Computing",

pages = "55--59",

editor = "Juan Corchado and \{De Paz\}, Juan and Miguel Rocha and Riverola, \{Florentino Fernandez\}",

booktitle = "2nd International Workshop on Practical Applications of Computational Biology and Bioinformatics (IWPACBB 2008)",

}

Nicolas, R, Golobardes, E , Fornells, A, Puig, S, Carrera, C & Malvehy, J 2009, Identification of relevant knowledge for characterizing the melanoma domain. in J Corchado, J De Paz, M Rocha & FF Riverola (eds), 2nd International Workshop on Practical Applications of Computational Biology and Bioinformatics (IWPACBB 2008). Advances in Soft Computing, vol. 49, pp. 55-59. https://doi.org/10.1007/978-3-540-85861-4_7

Identification of relevant knowledge for characterizing the melanoma domain. / Nicolas, Ruben; Golobardes, Elisabet ; Fornells, Albert et al.
2nd International Workshop on Practical Applications of Computational Biology and Bioinformatics (IWPACBB 2008). ed. / Juan Corchado; Juan De Paz; Miguel Rocha; Florentino Fernandez Riverola. 2009. p. 55-59 (Advances in Soft Computing; Vol. 49).

Research output: Book chapter › Conference contribution › peer-review

TY - GEN

T1 - Identification of relevant knowledge for characterizing the melanoma domain

AU - Nicolas, Ruben

AU - Golobardes, Elisabet

AU - Fornells, Albert

AU - Puig, Susana

AU - Carrera, Cristina

AU - Malvehy, Josep

PY - 2009

Y1 - 2009

N2 - Melanoma is one of the most important cancers to study in our current social context. This kind of cancer has increased its frequency in the last few years and its mortality is around twenty percent if it is not early treated. In order to improve the early diagnosis, the problem characterization using Machine Learning (ML) is crucial to identify melanoma patterns. Therefore we need to organize the data so that we can apply ML on it. This paper presents a detailed characterization based on the most relevant knowledge in melanomas problem and how to relate them to apply Data Mining techniques to aid medical diagnosis in melanoma and improve the research in this field.

AB - Melanoma is one of the most important cancers to study in our current social context. This kind of cancer has increased its frequency in the last few years and its mortality is around twenty percent if it is not early treated. In order to improve the early diagnosis, the problem characterization using Machine Learning (ML) is crucial to identify melanoma patterns. Therefore we need to organize the data so that we can apply ML on it. This paper presents a detailed characterization based on the most relevant knowledge in melanomas problem and how to relate them to apply Data Mining techniques to aid medical diagnosis in melanoma and improve the research in this field.

KW - Computer aided systems

KW - Health information systems

KW - Knowledge management and decision support systems

KW - Melanoma domain

UR - https://www.scopus.com/pages/publications/58149116942

U2 - 10.1007/978-3-540-85861-4_7

DO - 10.1007/978-3-540-85861-4_7

M3 - Conference contribution

AN - SCOPUS:58149116942

SN - 9783540858607

T3 - Advances in Soft Computing

SP - 55

EP - 59

BT - 2nd International Workshop on Practical Applications of Computational Biology and Bioinformatics (IWPACBB 2008)

A2 - Corchado, Juan

A2 - De Paz, Juan

A2 - Rocha, Miguel

A2 - Riverola, Florentino Fernandez

ER -

Nicolas R, Golobardes E , Fornells A, Puig S, Carrera C, Malvehy J. Identification of relevant knowledge for characterizing the melanoma domain. In Corchado J, De Paz J, Rocha M, Riverola FF, editors, 2nd International Workshop on Practical Applications of Computational Biology and Bioinformatics (IWPACBB 2008). 2009. p. 55-59. (Advances in Soft Computing). doi: 10.1007/978-3-540-85861-4_7

Identification of relevant knowledge for characterizing the melanoma domain

Abstract

Publication series

UN SDGs

Keywords

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