Prescriptive process monitoring aims to derive recommendations for optimizing complex processes. While previous studies have successfully used reinforcement learning techniques to derive actionable policies in business processes, care processes present unique challenges due to their dynamic and multifaceted nature. For example, at any stage of a care process, a multitude of actions is possible. In this study, we follow the Reinforcement Learning (RL) approach and present a general approach that uses event data to build and train Markov decision processes. We proposed three algorithms including one that takes the elapsed time into account when transforming an event log into a semi-Markov decision process. We evaluated the RL approach using an aggression incident data set. Specifically, the goal is to optimize staff member actions when clients are displaying different types of aggressive behavior. The Q-learning and SARSA are used to find optimal policies. Our results showed that the derived policies align closely with current practices while offering alternative options in specific situations. By employing RL in the context of care processes, we contribute to the ongoing efforts to enhance decision-making and efficiency in dynamic and complex environments.

In the recommender systems field, it is increasingly recognized that focusing on accuracy measures is limiting and misguided. Unsurprisingly, in recent years, the field has witnessed more interest in the research of values “beyond accuracy.” This trend is particularly pronounced in the news domain where recommender systems perform parts of the editorial function, required to uphold journalistic values of news organizations. In the literature, various values and approaches have been proposed and evaluated. This article reviews the current state of the proposed news recommender systems (NRS). We perform a systematic literature review, analyzing 183 papers. The primary aim is to study the development, scope, and focus of value-aware NRS over time. In contrast to previous surveys, we are particularly interested in identifying the range of values discussed and evaluated in the context of NRS and embrace an interdisciplinary view. We identified a total of 40 values, categorized into five value groups. Most research on value-aware NRS has taken an algorithmic approach, whereas conceptual discussions are comparably scarce. Often, algorithms are evaluated by accuracy-based metrics, but the values are not evaluated with respective measures. Overall, our work identifies research gaps concerning values that have not received much attention. Values need to be targeted on a more fine-grained and specific level.