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Main requirements of end-to-end deep learning models for biomedical time series classification in healthcare environments

  • The use of deep learning models with medical data is becoming more widespread. However, although numerous models have shown high accuracy in medical-related tasks, such as medical image recognition (e.g. radiographs), there are still many problems with seeing these models operating in a real healthcare environment. This article presents a series of basic requirements that must be taken into account when developing deep learning models for biomedical time series classification tasks, with the aim of facilitating the subsequent production of the models in healthcare. These requirements range from the correct collection of data, to the existing techniques for a correct explanation of the results obtained by the models. This is due to the fact that one of the main reasons why the use of deep learning models is not more widespread in healthcare settings is their lack of clarity when it comes to explaining decision making.

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Metadaten
Author of HS ReutlingenMartínez Madrid, Natividad; Serrano Alarcón, Ángel
URN:urn:nbn:de:bsz:rt2-opus4-38584
DOI:https://doi.org/10.1016/j.procs.2022.09.532
ISSN:1877-0509
Erschienen in:Procedia computer science
Publisher:Elsevier
Place of publication:Amsterdam
Document Type:Journal article
Language:English
Publication year:2022
Tag:biomedical time series; deep learning; healthcare
Volume:207
Issue:Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 26th International Conference KES2022
Page Number:9
First Page:3032
Last Page:3040
DDC classes:004 Informatik
Open access?:Ja
Licence (German):License Logo  Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International