TY - GEN
T1 - Layer-Wise Relevance Propagation in Multi-label Neural Networks to Identify COVID-19 Associated Coinfections
AU - Bello, Marilyn
AU - Aguilera, Yaumara
AU - Nápoles, Gonzalo
AU - García, María M.
AU - Bello, Rafael
AU - Vanhoof, Koen
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - COVID-19 has been affected worldwide since the end of 2019. Clinical studies have shown that a factor that increases its lethality is the existence of secondary infections. Coinfections associated with the infection SARS-CoV-2 are classified into bacterial infections and fungal infections. A patient may develop one, both, or neither. From a machine learning point of view, this is considered a multi-label classification problem. In this work, we propose a multi-label neural network able to detect such infections in a patient with SARS-CoV-2 and thus provide the medical community with a diagnosis to guide therapy in these patients. However, neural networks are often considered a “black box” model, as their strength in modeling complex interactions, also make their operation almost impossible to explain. Therefore, we propose three adaptations of the Layer-wise Relevance Propagation algorithm to explain multi-label neural networks. The inclusion of this post-hoc interpretability stage made it possible to identify significant input variables in a classifier output.
AB - COVID-19 has been affected worldwide since the end of 2019. Clinical studies have shown that a factor that increases its lethality is the existence of secondary infections. Coinfections associated with the infection SARS-CoV-2 are classified into bacterial infections and fungal infections. A patient may develop one, both, or neither. From a machine learning point of view, this is considered a multi-label classification problem. In this work, we propose a multi-label neural network able to detect such infections in a patient with SARS-CoV-2 and thus provide the medical community with a diagnosis to guide therapy in these patients. However, neural networks are often considered a “black box” model, as their strength in modeling complex interactions, also make their operation almost impossible to explain. Therefore, we propose three adaptations of the Layer-wise Relevance Propagation algorithm to explain multi-label neural networks. The inclusion of this post-hoc interpretability stage made it possible to identify significant input variables in a classifier output.
KW - Coinfections
KW - COVID-19
KW - Layer-wise Relevance Propagation
KW - Multi-label scenario
KW - Neural networks
UR - http://www.scopus.com/inward/record.url?scp=85119837746&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-89691-1_1
DO - 10.1007/978-3-030-89691-1_1
M3 - Conference contribution
AN - SCOPUS:85119837746
SN - 9783030896904
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 3
EP - 12
BT - Progress in Artificial Intelligence and Pattern Recognition - 7th International Workshop on Artificial Intelligence and Pattern Recognition, IWAIPR 2021, Proceedings
A2 - Hernández Heredia, Yanio
A2 - Milián Núñez, Vladimir
A2 - Ruiz Shulcloper, José
PB - Springer Science and Business Media Deutschland GmbH
T2 - 7th International Workshop on Artificial Intelligence and Pattern Recognition, IWAIPR 2021
Y2 - 5 October 2021 through 7 October 2021
ER -