An investigation of machine learning and neural computation paradigms in the design of clinical decision support systems (CDSSs)

Summrina K. Wajid; Amir Hussain; Bin Luo; Kaizhu Huang

doi:10.1007/978-3-319-49685-6_6

An investigation of machine learning and neural computation paradigms in the design of clinical decision support systems (CDSSs)

Summrina K. Wajid^*, Amir Hussain, Bin Luo, Kaizhu Huang

^*Corresponding author for this work

School of Advanced Technology

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

3 Citations (Scopus)

Abstract

This paper reviews the state of the art techniques for designing next generation CDSSs. CDSS can aid physicians and radiologists to better analyse and treat patients by combining their respective clinical expertise with complementary capabilities of the computers. CDSSs comprise many techniques from inter-desciplinary fields of medical image acquisition, image processing and pattern recognition, neural perception and pattern classifiers for medical data organization, and finally, analysis and optimization to enhance overall system performance. This paper discusses some of the current challenges in designing an efficient CDSS as well as some of the latest techniques that have been proposed to meet these challenges, primarily, by finding informative patterns in the medical dataset, analysing them and building a descriptive model of the object of interest, thus aiding in enhanced medical diagnosis.

Original language	English
Title of host publication	Advances in Brain Inspired Cognitive Systems - 8th International Conference, BICS 2016, Proceedings
Editors	Cheng-Lin Liu, Yi Zeng, Zhaoxiang Zhang, Kay Chen Tan, Bin Luo, Amir Hussain
Publisher	Springer Verlag
Pages	58-67
Number of pages	10
ISBN (Print)	9783319496849
DOIs	https://doi.org/10.1007/978-3-319-49685-6_6
Publication status	Published - 2016
Event	8th International Conference on Brain Inspired Cognitive Systems, BICS 2016 - Beijing, China Duration: 28 Nov 2016 → 30 Nov 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10023 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	8th International Conference on Brain Inspired Cognitive Systems, BICS 2016
Country/Territory	China
City	Beijing
Period	28/11/16 → 30/11/16

Keywords

Clinical decision support systems (CDSS)
Image processing
Machine learning
Neural computation
Neural networks

Access to Document

10.1007/978-3-319-49685-6_6

Cite this

Wajid, S. K., Hussain, A., Luo, B., & Huang, K. (2016). An investigation of machine learning and neural computation paradigms in the design of clinical decision support systems (CDSSs). In C.-L. Liu, Y. Zeng, Z. Zhang, K. C. Tan, B. Luo, & A. Hussain (Eds.), Advances in Brain Inspired Cognitive Systems - 8th International Conference, BICS 2016, Proceedings (pp. 58-67). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10023 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-49685-6_6

Wajid, Summrina K. ; Hussain, Amir ; Luo, Bin et al. / An investigation of machine learning and neural computation paradigms in the design of clinical decision support systems (CDSSs). Advances in Brain Inspired Cognitive Systems - 8th International Conference, BICS 2016, Proceedings. editor / Cheng-Lin Liu ; Yi Zeng ; Zhaoxiang Zhang ; Kay Chen Tan ; Bin Luo ; Amir Hussain. Springer Verlag, 2016. pp. 58-67 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "An investigation of machine learning and neural computation paradigms in the design of clinical decision support systems (CDSSs)",

abstract = "This paper reviews the state of the art techniques for designing next generation CDSSs. CDSS can aid physicians and radiologists to better analyse and treat patients by combining their respective clinical expertise with complementary capabilities of the computers. CDSSs comprise many techniques from inter-desciplinary fields of medical image acquisition, image processing and pattern recognition, neural perception and pattern classifiers for medical data organization, and finally, analysis and optimization to enhance overall system performance. This paper discusses some of the current challenges in designing an efficient CDSS as well as some of the latest techniques that have been proposed to meet these challenges, primarily, by finding informative patterns in the medical dataset, analysing them and building a descriptive model of the object of interest, thus aiding in enhanced medical diagnosis.",

keywords = "Clinical decision support systems (CDSS), Image processing, Machine learning, Neural computation, Neural networks",

author = "Wajid, {Summrina K.} and Amir Hussain and Bin Luo and Kaizhu Huang",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2016.; 8th International Conference on Brain Inspired Cognitive Systems, BICS 2016 ; Conference date: 28-11-2016 Through 30-11-2016",

year = "2016",

doi = "10.1007/978-3-319-49685-6_6",

language = "English",

isbn = "9783319496849",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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editor = "Cheng-Lin Liu and Yi Zeng and Zhaoxiang Zhang and Tan, {Kay Chen} and Bin Luo and Amir Hussain",

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Wajid, SK, Hussain, A, Luo, B & Huang, K 2016, An investigation of machine learning and neural computation paradigms in the design of clinical decision support systems (CDSSs). in C-L Liu, Y Zeng, Z Zhang, KC Tan, B Luo & A Hussain (eds), Advances in Brain Inspired Cognitive Systems - 8th International Conference, BICS 2016, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10023 LNAI, Springer Verlag, pp. 58-67, 8th International Conference on Brain Inspired Cognitive Systems, BICS 2016, Beijing, China, 28/11/16. https://doi.org/10.1007/978-3-319-49685-6_6

An investigation of machine learning and neural computation paradigms in the design of clinical decision support systems (CDSSs). / Wajid, Summrina K.; Hussain, Amir; Luo, Bin et al.
Advances in Brain Inspired Cognitive Systems - 8th International Conference, BICS 2016, Proceedings. ed. / Cheng-Lin Liu; Yi Zeng; Zhaoxiang Zhang; Kay Chen Tan; Bin Luo; Amir Hussain. Springer Verlag, 2016. p. 58-67 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10023 LNAI).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

TY - GEN

T1 - An investigation of machine learning and neural computation paradigms in the design of clinical decision support systems (CDSSs)

AU - Wajid, Summrina K.

AU - Hussain, Amir

AU - Luo, Bin

AU - Huang, Kaizhu

N1 - Publisher Copyright: © Springer International Publishing AG 2016.

PY - 2016

Y1 - 2016

N2 - This paper reviews the state of the art techniques for designing next generation CDSSs. CDSS can aid physicians and radiologists to better analyse and treat patients by combining their respective clinical expertise with complementary capabilities of the computers. CDSSs comprise many techniques from inter-desciplinary fields of medical image acquisition, image processing and pattern recognition, neural perception and pattern classifiers for medical data organization, and finally, analysis and optimization to enhance overall system performance. This paper discusses some of the current challenges in designing an efficient CDSS as well as some of the latest techniques that have been proposed to meet these challenges, primarily, by finding informative patterns in the medical dataset, analysing them and building a descriptive model of the object of interest, thus aiding in enhanced medical diagnosis.

AB - This paper reviews the state of the art techniques for designing next generation CDSSs. CDSS can aid physicians and radiologists to better analyse and treat patients by combining their respective clinical expertise with complementary capabilities of the computers. CDSSs comprise many techniques from inter-desciplinary fields of medical image acquisition, image processing and pattern recognition, neural perception and pattern classifiers for medical data organization, and finally, analysis and optimization to enhance overall system performance. This paper discusses some of the current challenges in designing an efficient CDSS as well as some of the latest techniques that have been proposed to meet these challenges, primarily, by finding informative patterns in the medical dataset, analysing them and building a descriptive model of the object of interest, thus aiding in enhanced medical diagnosis.

KW - Clinical decision support systems (CDSS)

KW - Image processing

KW - Machine learning

KW - Neural computation

KW - Neural networks

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SN - 9783319496849

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Advances in Brain Inspired Cognitive Systems - 8th International Conference, BICS 2016, Proceedings

A2 - Liu, Cheng-Lin

A2 - Zeng, Yi

A2 - Zhang, Zhaoxiang

A2 - Tan, Kay Chen

A2 - Luo, Bin

A2 - Hussain, Amir

PB - Springer Verlag

T2 - 8th International Conference on Brain Inspired Cognitive Systems, BICS 2016

Y2 - 28 November 2016 through 30 November 2016

ER -

Wajid SK, Hussain A, Luo B, Huang K. An investigation of machine learning and neural computation paradigms in the design of clinical decision support systems (CDSSs). In Liu CL, Zeng Y, Zhang Z, Tan KC, Luo B, Hussain A, editors, Advances in Brain Inspired Cognitive Systems - 8th International Conference, BICS 2016, Proceedings. Springer Verlag. 2016. p. 58-67. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-49685-6_6

An investigation of machine learning and neural computation paradigms in the design of clinical decision support systems (CDSSs)

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Keywords

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