@misc{10481/101597,
year = {2021},
url = {https://hdl.handle.net/10481/101597},
abstract = {Federated learning has a great potential to create solutions working over
different sources without data transfer. However current federated methods are not
explainable nor auditable. In this paper we propose a Federated data mining method
to discover association rules. More accurately, we define what we consider as
interesting itemsets and propose an algorithm to obtain them. This approach
facilitates the interoperability and reusability, and it is based on the accessibility to
data. These properties are quite aligned with the FAIR principles.},
keywords = {Electronic Health Record},
keywords = {Data minig},
keywords = {Privacy},
keywords = {Federated Learning},
title = {Federated Mining of Interesting Association Rules over EHRs},
doi = {https://doi.org/10.3233/SHTI210799},
author = {Molina, Carlos and Prados-Suárez, Belén and Martinez-Sanchez, Beatriz},
}