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Events recorded during the execution of a business process can be used to train models to predict, at run-time, the outcome of each execution of the process (a.k.a. case). In this setting, the outcome of a case may refer to whether a given case led to a customer complaint or not, or to a product return or other claims, or whether a case was completed on time or not. Existing approaches to train such predictive models do not take into account information about the prior experience of the (human) resources assigned to each task in the process. Instead, these approaches simply encode the resource who performs each task as a categorical (possibly one-hot encoded) feature. Yet, the experience of the resources involved in the execution of a case may clearly have an impact on the case outcome. For example, specialized resources or resources who are familiar with a given type of case, are more likely to execute the …