Sparse, interpretable and transparent predictive model identification for healthcare data analysis

Wei, H. orcid.org/0000-0002-4704-7346 (2019) Sparse, interpretable and transparent predictive model identification for healthcare data analysis. In: Rojas, I., Joya, G. and Catala, A., (eds.) Proceedings of the 2019 International Work-Conference on Artificial Neural Networks (Advances in Computational Intelligence). 2019 International Work-Conference on Artificial Neural Networks (Advances in Computational Intelligence), 12-14 Jun 2019, Gran Canaria, Spain. Lecture Notes in Computer Science, 15 (11506). Springer , pp. 103-114. ISBN 9783030205201

Abstract

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Authors/Creators:
Copyright, Publisher and Additional Information: © 2019 Springer. This is an author-produced version of a paper subsequently published in Lecture Notes in Computer Science. Uploaded in accordance with the publisher's self-archiving policy.
Keywords: System identification; Data-driven modelling; Prediction; Healthcare; Machine learning; NARMAX
Dates:
  • Accepted: 1 April 2019
  • Published (online): 16 May 2019
  • Published: 16 May 2019
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield)
Depositing User: Symplectic Sheffield
Date Deposited: 20 May 2019 14:19
Last Modified: 16 May 2020 00:38
Status: Published
Publisher: Springer
Series Name: Lecture Notes in Computer Science
Refereed: Yes
Identification Number: https://doi.org/10.1007/978-3-030-20521-8_9

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