Background Health recommender systems (HRSs) are information retrieval systems that
provide users with relevant items according to the users' needs, which can motivate and …

This systematic review aims to characterize the utility of machine learning to identify the
predictors of smoking cessation outcomes and identify the machine learning methods …

The generation of biomedical data is of such magnitude that its retrieval and analysis have
posed several challenges. A survey of recommender system (RS) approaches in biomedical …

Background People often look online for information about health concerns, but the vast
amount of available and unregulated information can cause misinformation and potential …

Background: Recent studies have shown positive, though small, clinical effects of digital
smoking cessation (SC) interventions for cancer survivors. However, research on …

This scoping review explores the integration of Artificial Intelligence (AI) with communication,
behavioral, and social theories to enhance health behavior interventions. A systematic …

Background: Health recommender systems (HRSs) are intelligent systems that can be used
to tailor digital health interventions. We compared two HRSs to assess their impact providing …

In the age of artificial intelligence and large datasets, information retrieval by querying large
databases is an impossible task for the common user due to the information overload …