Use of patient generated data from social media and collaborative filtering for preferences elicitation in shared decision making

Enea Parimbelli, Silvana Quaglini, Carlo Napolitano, Silvia Priori, Riccardo Bellazzi, John Holmes

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

With the increasing demand for personalization in clinical decision support system, one of the most challenging tasks is effective patient preferences elicitation. In the context of the MobiGuide project, within a medical application related to atrial fibrillation, a decision support system has been developed for both doctors and patients. In particular, we support shared decision-making, by integrating decision tree models with a dedicated tool for utility coefficients elicitation. In this paper we focus on the decision problem regarding the choice of anticoagulant therapy for low risk non-valvular atrial fibrillation patients. In addition to the traditional methods, such as time trade-off and standard gamble, an alternative way for preferences elicitation is proposed, exploiting patients' self-reported data in healthrelated social media as the main source of information.

Original languageEnglish
Title of host publicationAAAI Fall Symposium - Technical Report
PublisherAI Access Foundation
Pages35-38
Number of pages4
VolumeFS
ISBN (Print)9781577356936
Publication statusPublished - 2014
Event2014 AAAI Fall Symposium - Arlington, United States
Duration: Nov 13 2014Nov 15 2014

Other

Other2014 AAAI Fall Symposium
CountryUnited States
CityArlington
Period11/13/1411/15/14

ASJC Scopus subject areas

  • Engineering(all)

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    Parimbelli, E., Quaglini, S., Napolitano, C., Priori, S., Bellazzi, R., & Holmes, J. (2014). Use of patient generated data from social media and collaborative filtering for preferences elicitation in shared decision making. In AAAI Fall Symposium - Technical Report (Vol. FS, pp. 35-38). AI Access Foundation.