TY - GEN
T1 - A Multi-label Classification Study for the Prediction of Long-COVID Syndrome
AU - Dossena, Marco
AU - Irwin, Christopher
AU - Piovesan, Luca
AU - Portinale, Luigi
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - We present a study about the prediction of long-COVID sequelae through multi-label classification (MLC). Data about more than 300 patients have been collected during a long-COVID study at Ospedale Maggiore of Novara (Italy), considering their baseline situation, as well as their condition on acute COVID-19 onset. The goal is to predict the presence of specific long-COVID sequelae after a one-year follow-up. To amplify the representativeness of the analysis, we carefully investigated the possibility of augmenting the dataset, by considering situations where different levels in the number of complications could arise. MLSmote under six different policies of data augmentation has been considered, and a representative set of MLC approaches have been tested on all the available datasets. Results have been evaluated in terms of Accuracy, Exact match, Hamming Score and macro-averaged AUC; they show that MLC methods can actually be useful for the prediction of specific long-COVID sequelae, under the different conditions represented by the different considered datasets.
AB - We present a study about the prediction of long-COVID sequelae through multi-label classification (MLC). Data about more than 300 patients have been collected during a long-COVID study at Ospedale Maggiore of Novara (Italy), considering their baseline situation, as well as their condition on acute COVID-19 onset. The goal is to predict the presence of specific long-COVID sequelae after a one-year follow-up. To amplify the representativeness of the analysis, we carefully investigated the possibility of augmenting the dataset, by considering situations where different levels in the number of complications could arise. MLSmote under six different policies of data augmentation has been considered, and a representative set of MLC approaches have been tested on all the available datasets. Results have been evaluated in terms of Accuracy, Exact match, Hamming Score and macro-averaged AUC; they show that MLC methods can actually be useful for the prediction of specific long-COVID sequelae, under the different conditions represented by the different considered datasets.
KW - data augmentation
KW - long-COVID syndrome
KW - multi-label classification
UR - http://www.scopus.com/inward/record.url?scp=85177207518&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-47546-7_18
DO - 10.1007/978-3-031-47546-7_18
M3 - Conference contribution
AN - SCOPUS:85177207518
SN - 9783031475450
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 265
EP - 277
BT - AIxIA 2023 – Advances in Artificial Intelligence - 22nd International Conference of the Italian Association for Artificial Intelligence, AIxIA 2023, Proceedings
A2 - Basili, Roberto
A2 - Limongelli, Carla
A2 - Lembo, Domenico
A2 - Orlandini, Andrea
PB - Springer Science and Business Media Deutschland GmbH
T2 - 22nd International Conference of the Italian Association for Artificial Intelligence, AIxIA 2023
Y2 - 6 November 2023 through 9 November 2023
ER -