Extracting Temporal Relationships in EHR: Application to COVID-19 Patients Molina Fernández, Carlos Prados Suárez, María Belén Temporal Association Rules (TAR) Multidimensional Model Complexity COVID-19 Association rules are one of the most used data mining techniques. The first proposals have considered relations over time in different ways, resulting in the so-called Temporal Association Rules (TAR). Although there are some proposals to extract association rules in OLAP systems, to the best of our knowledge, there is no method proposed to extract temporal association rules over multidimensional models in these kinds of systems. In this paper we study the adaptation of TAR to multidimensional structures, identifying the dimension that establishes the number of transactions and how to find time relative correlations between the other dimensions. A new method called COGtARE is presented as an extension of a previous approach proposed to reduce the complexity of the resulting set of association rules. The method is tested in application to COVID-19 patients data. 2023-10-31T07:35:18Z 2023-10-31T07:35:18Z 2023 conference output Molina, C., & Prados-Suarez, B. (2023). Extracting Temporal Relationships in EHR: Application to COVID-19 Patients. Studies in Health Technology and Informatics, 302, 546-550. [doi:10.3233/SHTI230202] https://hdl.handle.net/10481/85345 10.3233/SHTI230202 eng http://creativecommons.org/licenses/by-nc/4.0/ open access Atribución-NoComercial 4.0 Internacional IOS Press