Exploiting Recommender Systems in Collaborative Healthcare

D'AURIA, Daniela, Mouzhi GE a 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, s. 71-82. ISBN 978-3-030-30142-2. Dostupné z: https://dx.doi.org/10.1007/978-3-030-30143-9_6.

Základní údaje

Originální název

Exploiting Recommender Systems in Collaborative Healthcare

Autoři

D'AURIA, Daniela (380 Itálie), Mouzhi GE (156 Čína, garant, domácí) a Fabio PERSIA

Vydání

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

Nakladatel

Springer Communications in Computer and Information Science

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Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Německo

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

tištěná verze "print"

Kód RIV

RIV/00216224:14330/19:00110607

Organizační jednotka

Fakulta informatiky

ISBN

978-3-030-30142-2

ISSN

DOI

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

Klíčová slova anglicky

Collaborative healthcare; Medical auxiliaries; Recommender systems

Příznaky

Mezinárodní význam, Recenzováno

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Anotace

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.