Exploiting Recommender Systems in Collaborative Healthcare

D'AURIA, Daniela, Mouzhi GE and Fabio PERSIA. Exploiting Recommender Systems in Collaborative Healthcare. In Proceedings of the 16th International Symposium on Pervasive Systems, Algorithms and Networks. Naples, Italy: Springer Communications in Computer and Information Science, 2019, p. 71-82. ISBN 978-3-030-30142-2. Available from: https://dx.doi.org/10.1007/978-3-030-30143-9_6.

Basic information

Original name

Exploiting Recommender Systems in Collaborative Healthcare

Authors

D'AURIA, Daniela (380 Italy), Mouzhi GE (156 China, guarantor, belonging to the institution) and Fabio PERSIA

Edition

Naples, Italy, Proceedings of the 16th International Symposium on Pervasive Systems, Algorithms and Networks, p. 71-82, 12 pp. 2019

Publisher

Springer Communications in Computer and Information Science

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Other information

Language

English

Type of outcome

Stať ve sborníku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

Germany

Confidentiality degree

není předmětem státního či obchodního tajemství

Publication form

printed version "print"

RIV identification code

RIV/00216224:14330/19:00110607

Organization unit

Faculty of Informatics

ISBN

978-3-030-30142-2

ISSN

DOI

http://dx.doi.org/10.1007/978-3-030-30143-9_6

Keywords in English

Collaborative healthcare; Medical auxiliaries; Recommender systems

Tags

International impact, Reviewed

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Abstract

V originále

With the development of new medical auxiliaries such as virtual reality and surgery robotics, recommender systems are emerged to interact with the medical auxiliaries and support doctor’s decisions and operations, especially in collaborative healthcare, recommender systems can interactively take into account the preferences and concerns from both patients and doctors. However, how to apply and integrate recommender systems is still not clear in collaborative healthcare. Therefore, from practical perspective this paper investigates the application of recommender systems in three typical collaborative healthcare domains, which are augmented/virtual reality, medicine and surgery robotics. The results not only provide the insights of how to integrate recommender systems with healthcare auxiliaries but also discuss the practical guidance of how to design recommender systems in collaborative healthcare.