Electronic Healthcare. Second International ICST Conference, eHealth 2009, Istanbul, Turkey, September 23-15, 2009, Revised Selected Papers

Research Article

Personality Diagnosis for Personalized eHealth Services

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  • @INPROCEEDINGS{10.1007/978-3-642-11745-9_25,
        author={Fabio Cortellese and Marco Nalin and Angelica Morandi and Alberto Sanna and Floriana Grasso},
        title={Personality Diagnosis for Personalized eHealth Services},
        proceedings={Electronic Healthcare. Second International ICST Conference, eHealth 2009, Istanbul, Turkey, September 23-15, 2009, Revised Selected Papers},
        proceedings_a={E-HEALTH},
        year={2012},
        month={5},
        keywords={Personalization Personality Diagnosis Motivation Strategy Collaborative Filtering Natural Language Processing Contextualization Dynamic Clustering},
        doi={10.1007/978-3-642-11745-9_25}
    }
    
  • Fabio Cortellese
    Marco Nalin
    Angelica Morandi
    Alberto Sanna
    Floriana Grasso
    Year: 2012
    Personality Diagnosis for Personalized eHealth Services
    E-HEALTH
    Springer
    DOI: 10.1007/978-3-642-11745-9_25
Fabio Cortellese1,*, Marco Nalin2,*, Angelica Morandi2,*, Alberto Sanna2,*, Floriana Grasso1,*
  • 1: University of Liverpool
  • 2: Fondazione Centro San Raffaele del Monte Tabor, eServices for Life and Health
*Contact email: fabiocortellese@gmail.com, marco.nalin@hsr.it, angelica.morandi@hsr.it, alberto.sanna@hsr.it, floriana@liverpool.ac.uk

Abstract

In this paper we present two different approaches to personality diagnosis, for the provision of innovative personalized services, as used in a case study where diabetic patients were supported in the improvement of physical activity in their daily life. The first approach presented relies on a of the population, with a specific motivation strategy designed for each cluster. The second approach relies on a clustering, making use of recommendation systems and algorithms, like Collaborative Filtering. We discuss pro and cons of each approach and a possible combination of the two, as the most promising solution for this and other personalization services in eHealth.