AI for health modeling, health prediction, and personalized intervention
We develop AI algorithms to enable descriptive, predictive, and prescriptive health data analytics for several applications. For example, we develop predictive algorithms able to identify people at risk of developing chronic disease (e.g., diabetes, chronic kidney disease, heart failure); we build data-driven personalized intervention programs that are tailored to the individuals’ needs using clustering approaches; and we study the causal relationships – beyond associations – between, e.g., physical conditions and mental health, and between patient status and patient outcomes. We develop integrated systems, using wearable and environmental IoT sensors and AI algorithms to monitor the individual health and wellbeing and enable individualized plans for patient treatment and follow up.
mHealth apps evaluation
In the rapidly growing market of mobile health apps (>300,000 on the market), we study and develop original methods, including automated ones, that can effectively retrieve, structure, and process information related to health apps towards the definition of minimum benchmarks for app quality and for user oriented information. For example, we develop evaluation frameworks for apps in specific application areas (e.g., diabetes prevention, nutrition, audiology); we develop automated methods to extract relevant apps’ features from large databases (e.g., from unstructured text information available on the web); we contribute to the development of recommendations for app quality and for software as medical device (SaMD).
eHealth for Hearing
We develop novel, data-driven online- and mobile-based approaches for hearing testing and hearing rehabilitation, for example automated, language-independent tests for widespread online screening, machine-learning algorithms for hearing screening and patient clustering, novel systems and data analytics methods for the analysis of cognitive and listening effort as well as for the assessment of emotions and self-confidence in individuals with hearing impairment and in hearing aid recipients.