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Federated Mining of Interesting Association Rules Over EHRs
Carlos Molina, Belen Prados-Suarez, Beatriz Martinez-Sanchez
3 - 7
10.3233/SHTI210799
Research Article
Studies in Health Technology and Informatics
Volume 287: Applying the FAIR Principles to Accelerate Health Research in Europe in the Post COVID-19 Era
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.

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