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Deep neural architectures for prediction in healthcare

Deep neural architectures for prediction in healthcare

Kollias, Dimitrios ORCID: 0000-0002-8188-3751, Tagaris, Athanasios, Stafylopatis, Andreas, Kollias, Stefanos ORCID: 0000-0003-2899-0598 and Tagaris, Georgios (2017) Deep neural architectures for prediction in healthcare. Complex & Intelligent Systems, 4 (2). pp. 119-131. ISSN 2199-4536 (Print), 2198-6053 (Online) (doi:https://doi.org/10.1007/s40747-017-0064-6)

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Abstract

This paper presents a novel class of systems assisting diagnosis and personalised assessment of diseases in healthcare. The targeted systems are end-to-end deep neural architectures that are designed (trained and tested) and subsequently used as whole systems, accepting raw input data and producing the desired outputs. Such architectures are state-of-the-art in image analysis and computer vision, speech recognition and language processing. Their application in healthcare for prediction and diagnosis purposes can produce high accuracy results and can be combined with medical knowledge to improve effectiveness, adaptation and transparency of decision making. The paper focuses on neurodegenerative diseases, particularly Parkinson’s, as the development model, by creating a new database and using it for training, evaluating and validating the proposed systems. Experimental results are presented which illustrate the ability of the systems to detect and predict Parkinson’s based on medical imaging information.

Item Type: Article
Uncontrolled Keywords: deep learning, convolutional recurrent neural networks, prediction, adaptation, clustering, Parkinson’s, healthcare
Subjects: Q Science > QA Mathematics
Faculty / Department / Research Group: Faculty of Liberal Arts & Sciences
Faculty of Liberal Arts & Sciences > School of Computing & Mathematical Sciences (CAM)
Last Modified: 26 Nov 2020 23:01
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
Selected for REF2021: REF 5
URI: http://gala.gre.ac.uk/id/eprint/29428

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