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Machine Learning Approaches to Automated Medical Decision Support Systems
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Author(s): Nuno Pombo (Instituto de Telecomunicações, Covilhã, Portugal), Nuno Garcia (Instituto de Telecomunicações, Covilhã, Portugal), Kouamana Bousson (University of Beira Interior, Portugal)and Virginie Felizardo (Instituto de Telecomunicações, Covilhã, Portugal)
Copyright: 2015
Pages: 21
Source title:
Handbook of Research on Artificial Intelligence Techniques and Algorithms
Source Author(s)/Editor(s): Pandian Vasant (University of Technology Petronas, Malaysia)
DOI: 10.4018/978-1-4666-7258-1.ch006
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Abstract
This chapter provides an overview of the Machine Learning (ML) concepts in the clinical field which data may be collected, either by Health Care Professionals (HCP) or patients. These data may include activities and medication reminders, objective measurement of physiological parameters, feedback based on observed patterns, questionnaires and scores that require computational processes that give rise to useful information capable of supporting clinical decision making. The chapter describes ML in terms of learning concepts emphasizing the following approaches: supervised, unsupervised, semi-supervised, and reinforcement learning. The principles of concept classification are explained and the mathematical concepts of several methodologies are presented, such as neural networks and support vector machine among other techniques. Finally, a case study based on a radial basis function neural network aiming at the estimation of ECG waveform is presented. The proposed method reveals its suitability to support HCP on clinical decisions and practices.
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