TY - GEN
T1 - An Inception-Based Architecture for Haemodialysis Time Series Classification
AU - Leonardi, Giorgio
AU - Montani, Stefania
AU - Striani, Manuel
N1 - Publisher Copyright:
© 2021, IFIP International Federation for Information Processing.
PY - 2021
Y1 - 2021
N2 - Classifying haemodialysis sessions, on the basis of the evolution of specific clinical variables over time, allows the physician to identify patients that are being treated inefficiently, and that may need additional monitoring or corrective interventions. In this paper, we propose a deep learning approach to clinical time series classification, in the haemodialysis domain. Specifically, grounding on our previous experience in adopting convolutional neural networks on haemodialysis time series, we have defined an inception-based architecture, able to exploit kernels of different sizes in parallel. The proposed architecture has outperformed the results obtained by resorting both to a more standard convolutional neural network, and to the state of the art approach ROCKET, since we reached higher accuracy values, coupled with a good Matthews Correlation Coefficient.
AB - Classifying haemodialysis sessions, on the basis of the evolution of specific clinical variables over time, allows the physician to identify patients that are being treated inefficiently, and that may need additional monitoring or corrective interventions. In this paper, we propose a deep learning approach to clinical time series classification, in the haemodialysis domain. Specifically, grounding on our previous experience in adopting convolutional neural networks on haemodialysis time series, we have defined an inception-based architecture, able to exploit kernels of different sizes in parallel. The proposed architecture has outperformed the results obtained by resorting both to a more standard convolutional neural network, and to the state of the art approach ROCKET, since we reached higher accuracy values, coupled with a good Matthews Correlation Coefficient.
UR - http://www.scopus.com/inward/record.url?scp=85112602659&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-79157-5_17
DO - 10.1007/978-3-030-79157-5_17
M3 - Conference contribution
AN - SCOPUS:85112602659
SN - 9783030791568
T3 - IFIP Advances in Information and Communication Technology
SP - 194
EP - 203
BT - Artificial Intelligence Applications and Innovations. AIAI 2021 IFIP WG 12.5 International Workshops - 5G-PINE 2021, AI-BIO 2021, DAAI 2021, DARE 2021, EEAI 2021, and MHDW 2021, Proceedings
A2 - Maglogiannis, Ilias
A2 - Macintyre, John
A2 - Iliadis, Lazaros
PB - Springer Science and Business Media Deutschland GmbH
T2 - 17th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2021, 6th Workshop on 5G-Putting Intelligence to the Network Edge, 5G-PINE 2021, Artificial Intelligence in Biomedical Engineering and Informatics Workshop, AI-BIO 2021, Workshop on Defense Applications of AI, DAAI 2021, Distributed AI for Resource-Constrained Platforms Workshop, DARE 2021, Energy Efficiency and Artificial Intelligence Workshop, EEAI 2021, and 10th Mining Humanistic Data Workshop, MHDW 2021
Y2 - 25 June 2021 through 27 June 2021
ER -