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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:Ángel Serrano AlarcónORCiD, Natividad Martínez MadridORCiD, Ralf SeepoldORCiDGND, Juan Antonio OrtegaORCiD
DOI:https://doi.org/10.1016/j.procs.2022.09.532
ISSN:1877-0509
Parent Title (English):Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 26th International Conference, KES2022, 7 - 9 September 2022, Verona, Italy (Procedia Computer Science)
Volume:207
Publisher:Elsevier
Place of publication:Amsterdam
Document Type:Conference Proceeding
Language:English
Year of Publication:2022
Release Date:2022/11/14
Tag:Deep learning; Biomedical time series; Healthcare
First Page:3032
Last Page:3040
Note:
Corresponding author: Ángel Serrano Alarcón
Institutes:Institut für Angewandte Forschung - IAF
Open Access?:Ja
Relevance:Peer reviewed nach anderen Listungen (mit Nachweis zum Peer Review Verfahren)
Licence (German):License LogoCreative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International