Collaborative filtering for estimating health related utilities in decision support systems

Enea Parimbelli, Silvana Quaglini, Riccardo Bellazzi, John H. Holmes

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

Abstract

A distinctive feature of most advanced clinical decision support systems is the ability to adapt to habits and preferences of patients. However effective preferences elicitation is still among the most challenging tasks to achieve fully personalized guidance. On the other hand availability of data related to patients’ lives and habits is steadily increasing, making its exploitation an interesting opportunity for such purposes. In the MobiGuide project decision trees are used to implement shared-decision making using utility coefficients to incorporate patient preferences in the model. The main focus of this paper is the effort devoted to enhance traditional elicitation techniques proposing a methodology to predict patients’ health-related utility coefficients. In particular we describe a recommender system, based on collaborative filtering, capable of estimating utilities by means of integrating different data sources such as medical surveys, questionnaires and utility elicitation tools along with patient self-reported experiences in the form of natural language.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages106-110
Number of pages5
Volume9105
ISBN (Print)9783319195506
DOIs
Publication statusPublished - 2015
Event15th Conference on Artificial Intelligence in Medicine, AIME 2015 - Pavia, Italy
Duration: Jun 17 2015Jun 20 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9105
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other15th Conference on Artificial Intelligence in Medicine, AIME 2015
CountryItaly
CityPavia
Period6/17/156/20/15

Keywords

  • Collaborative filtering
  • DSS personalization
  • Health related utility

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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  • Cite this

    Parimbelli, E., Quaglini, S., Bellazzi, R., & Holmes, J. H. (2015). Collaborative filtering for estimating health related utilities in decision support systems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9105, pp. 106-110). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9105). Springer Verlag. https://doi.org/10.1007/978-3-319-19551-3_13