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.
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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
Other information
Original language English
Type of outcome Proceedings paper
Field of Study 10201 Computer sciences, information science, bioinformatics
Country of publisher Germany
Confidentiality degree is not subject to a state or trade secret
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 1865-0929
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
Changed by Changed by: RNDr. Pavel Šmerk, Ph.D., učo 3880. Changed: 6/5/2020 15:34.
Abstract
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.
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