Cataldo Musto

Dr. Cataldo Musto

Department of Computer Science, University of Bari "Aldo Moro", Via E.Orabona, 4-70126 Bari, Italy
orcid

Research Focus & Metrics

Academic Career Length

18Years (2009-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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7 March 2019 in User Modeling and User-Adapted Interaction
Verified
Journal Article
Open access
PDF file
PLPasquale Lops
Pasquale Lops
DJDietmar Jannach
Dietmar Jannach
Cataldo MustoCataldo Musto
Cataldo Musto
TBToine Bogers
Toine Bogers
MKMarijn Koolen
Marijn Koolen

1 January 2015
Verified
Book Chapter
Closed access
PDF file
MGMarco De Gemmis
Marco De Gemmis
PLPasquale Lops
Pasquale Lops
Cataldo MustoCataldo Musto
Cataldo Musto
FNFedelucio Narducci
Fedelucio Narducci
Giovanni SemeraroGiovanni Semeraro
Giovanni Semeraro

1 September 2015 in Information Processing & Management
Verified
Journal Article
Closed access
MGMarco De Gemmis
Marco De Gemmis
PLPasquale Lops
Pasquale Lops
Giovanni SemeraroGiovanni Semeraro
Giovanni Semeraro
Cataldo MustoCataldo Musto
Cataldo Musto

1 September 2015 in Decision Support Systems
Verified
Journal Article
Closed access
Cataldo MustoCataldo Musto
Cataldo Musto
Giovanni SemeraroGiovanni Semeraro
Giovanni Semeraro
PLPasquale Lops
Pasquale Lops
MGMarco De Gemmis
Marco De Gemmis
GLGeorgios Lekkas
Georgios Lekkas


8 June 2020 in IEEE Access
Verified
Journal Article
Open access
Marco PolignanoMarco Polignano
Marco Polignano
FNFedelucio Narducci
Fedelucio Narducci
AIAndrea Iovine
Andrea Iovine
Cataldo MustoCataldo Musto
Cataldo Musto
MGMarco De Gemmis
Marco De Gemmis
GSGiovanni Semeraro
Giovanni Semeraro
In this article, we present HealthAssistantBot, an intelligent virtual assistant able to talk with patients in order to understand their symptomatology, suggest doctors, and monitor treatments and health parameters. In a simple way, by exploiting a natural language-based interaction, the system allows the user to create her health profile, to describe her symptoms, to search for doctors or to simply remember a treatment to follow. Specifically, our methodology exploits machine learning techniques to process users symptoms and to automatically infer her diseases. Next, the information obtained is used by our recommendation algorithm to identify the nearest doctor who can best treat the user's condition, considering the community data. In the experimental session we evaluated our HealthAssistantBot with both an offline and online evaluation. In the first case, we assessed the performance of our internal components, while in the second one we carried out a study involving 102 subjects who interacted with the conversational agent in a daily use scenario. Results are encouraging and showed the effectiveness of the strategy in supporting the patients in taking care of their health.