An Agent-based Decision Support for a Vaccination Campaign

We explore the Covid-19 diffusion with an agent-based model of an Italian region with a population on a scale of 1:1000. We also simulate different vaccination strategies. From a decision support system perspective, we investigate the adoption of artificial intelligence techniques to provide suggestions about more effective policies. We adopt the widely used multi-agent programmable modeling environment NetLogo, adding genetic algorithms to evolve the best vaccination criteria. The results suggest a promising methodology for defining vaccine rates by population types over time. The results are encouraging towards a more extensive application of agent-oriented methods in public healthcare policies

Improve Hospital Management Through Process Mining, Optimization, and Simulation: the CH4I-PM Project

The growing digitalization of society opens up the exploitation of new IT techniques in the healthcare sector. This report presents an application of AI techniques such as prediction, optimization, and automated knowledge extraction with process mining from hospital information system data. In addition, a simulation effort with Building Information Modeling and Agent-Based Modeling techniques has been performed. The present report describes practical cases and the lesson learned from planning, management, and coordination activities of the project as a whole

Processing Affect in Social Media: A Comparison of Methods to Distinguish Emotions in Tweets

Emotion analysis in social media is challenging. While most studies focus on positive and negative sentiments, the differentiation between emotions is more difficult. We investigate the problem as a collection of binary classification tasks on the basis of four opposing emotion pairs provided by Plutchik. We processed the content of messages by three alternative methods: structural and lexical features, latent factors, and natural language processing. The final prediction is suggested by classifiers deriving from the state of the art in machine learning. Results are convincing in the possibility to distinguish the emotions pairs in social media.</jats:p