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Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings
Peer Review Status: Refereed
Author(s): Wajid, Summrina
Hussain, Amir
Luo, Bin
Huang, Kaizhu
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Title: An investigation of machine learning and neural computation paradigms in the design of clinical decision support systems (CDSSs)
Editor(s): Liu, CL
Hussain, A
Luo, B
Tan, KC
Zeng, Y
Zhang, Z
Citation: Wajid S, 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: Liu C, Hussain A, Luo B, Tan K, Zeng Y & Zhang Z (eds.) Advances in Brain Inspired Cognitive Systems. BICS 2016. Lecture Notes in Computer Science, 10023. BICS2016: 8th International Conference on Brain-Inspired Cognitive Systems, Beijing, China, 28.11.2016-30.11.2016. Cham, Switzerland: Springer, pp. 58-67.;
Issue Date: 2016
Series/Report no.: Lecture Notes in Computer Science, 10023
Conference Name: BICS2016: 8th International Conference on Brain-Inspired Cognitive Systems
Conference Dates: 2016-11-28 - 2016-11-30
Conference Location: Beijing, China
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.
Status: VoR - Version of Record
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