TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Serrano Alarcón, Ángel
A1  - Martínez Madrid, Natividad
A1  - Seepold, Ralf
A1  - Ortega, Juan Antonio
T1  - Main requirements of end-to-end deep learning models for biomedical time series classification in healthcare environments
T2  - Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 26th International Conference, KES2022, 7 - 9 September 2022, Verona, Italy (Procedia Computer Science)
N2  - 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.
KW  - Deep learning
KW  - Biomedical time series
KW  - Healthcare
Y1  - 2022
SN  - 1877-0509
SS  - 1877-0509
U6  - https://doi.org/10.1016/j.procs.2022.09.532
DO  - https://doi.org/10.1016/j.procs.2022.09.532
N1  - Corresponding author: Ángel Serrano Alarcón
VL  - 207
SP  - 3032
EP  - 3040
PB  - Elsevier
CY  - Amsterdam
ER  -