Analysis of Machine Learning Models Predicting Quality of Life for Cancer Patients-Reference-Cited by-同舟云学术

Analysis of Machine Learning Models Predicting Quality of Life for Cancer Patients

Published:2021-11 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 13th International Conference on Management of Digital EcoSystems
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Author:

Savić Miloš¹,Kurbalija Vladimir¹,Ilić Mihailo¹,Ivanović Mirjana¹,Jakovetić Dušan¹,Valachis Antonios²,Autexier Serge³,Rust Johannes³,Kosmidis Thanos⁴

Affiliation:

1. Department of Mathematics and Informatics, University of Novi Sad, Novi Sad, Serbia

2. Department of Oncology, Faculty of Medicine and Health, Örebro University SE 70182 Örebro, Sweden

3. German Research Center for Artificial Intelligence GmbH, Cyber-Physical Systems Bremen, Germany

4. Care Across Ltd London, England

Funder

European Union?s Horizon 2020 research and innovation programme

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3444757.3485103

Reference20 articles.

1. Data mining and electronic devices applied to quality of life related to health data

2. A Survey on Differentially Private Machine Learning [Review Article]

3. Peter Kairouz H. Brendan McMahan Brendan Avent Aurélien Bellet Mehdi Bennis Arjun Nitin Bhagoji Kallista Bonawitz Zachary Charles Graham Cormode Rachel Cummings Rafael G. L. D'Oliveira Hubert Eichner Salim El Rouayheb David Evans Josh Gardner Zachary Garrett Adrià Gascón Badih Ghazi Phillip B. Gibbons Marco Gruteser Zaid Harchaoui Chaoyang He Lie He Zhouyuan Huo Ben Hutchinson Justin Hsu Martin Jaggi Tara Javidi Gauri Joshi Mikhail Khodak Jakub Konečný Aleksandra Korolova Farinaz Koushanfar Sanmi Koyejo Tancrède Lepoint Yang Liu Prateek Mittal Mehryar Mohri Richard Nock Ayfer Özgür Rasmus Pagh Mariana Raykova Hang Qi Daniel Ramage Ramesh Raskar Dawn Song Weikang Song Sebastian U. Stich Ziteng Sun Ananda Theertha Suresh Florian Tramèr Praneeth Vepakomma Jianyu Wang Li Xiong Zheng Xu Qiang Yang Felix X. Yu Han Yu and Sen Zhao. 2021. Advances and Open Problems in Federated Learning. arXiv:1912.04977 [cs.LG] Peter Kairouz H. Brendan McMahan Brendan Avent Aurélien Bellet Mehdi Bennis Arjun Nitin Bhagoji Kallista Bonawitz Zachary Charles Graham Cormode Rachel Cummings Rafael G. L. D'Oliveira Hubert Eichner Salim El Rouayheb David Evans Josh Gardner Zachary Garrett Adrià Gascón Badih Ghazi Phillip B. Gibbons Marco Gruteser Zaid Harchaoui Chaoyang He Lie He Zhouyuan Huo Ben Hutchinson Justin Hsu Martin Jaggi Tara Javidi Gauri Joshi Mikhail Khodak Jakub Konečný Aleksandra Korolova Farinaz Koushanfar Sanmi Koyejo Tancrède Lepoint Yang Liu Prateek Mittal Mehryar Mohri Richard Nock Ayfer Özgür Rasmus Pagh Mariana Raykova Hang Qi Daniel Ramage Ramesh Raskar Dawn Song Weikang Song Sebastian U. Stich Ziteng Sun Ananda Theertha Suresh Florian Tramèr Praneeth Vepakomma Jianyu Wang Li Xiong Zheng Xu Qiang Yang Felix X. Yu Han Yu and Sen Zhao. 2021. Advances and Open Problems in Federated Learning. arXiv:1912.04977 [cs.LG]

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