Predicting and Understanding Care Levels of Elderly People with Machine Learning-Reference-Cited by-同舟云学术

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Predicting and Understanding Care Levels of Elderly People with Machine Learning

Published:2023 Issue: Volume: Page:39-54
ISSN:0302-9743
Container-title:HCI International 2023 – Late Breaking Papers
language:
Short-container-title:

Author:

Heiba Naguib^ORCID,Mohamad Yehya^ORCID,Velasco Carlos A.^ORCID,Gappa Henrike^ORCID,Berlage Thomas,Geisler Sandra^ORCID

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-48041-6_4

Reference23 articles.

1. European commission: communication on the European care strategy (2022). https://www.epsu.org/sites/default/files/event/files/9%20Sept_European%20care%20strategy_EASPD%20webinar.pdf. Accessed 22 June 2023

2. Mohamed, Y., et al.: How to overcome lack of health record data and privacy obstacles in initial phases of medical data analysis projects. Comput. Inform. 41(1), 233–252 (2022). https://doi.org/10.31577/cai_2022_1_233

3. Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001). https://doi.org/10.1023/A:1010933404324

4. Statistische bundesamt: long-term care (2023). https://www.destatis.de/EN/Themes/Society-Environment/Health/Long-Term-Care/_node.html. Accessed 22 June 2023

5. Statistische bundesamt: people in need of long-term care (2019). https://www.destatis.de/EN/Themes/Society-Environment/Health/Long-Term-Care/Tables/people-long-term-care.html#fussnote-1-50564. Accessed 22 June 2023

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