Explainability of Machine Learning in Work Disability Risk Prediction-Reference-Cited by-同舟云学术

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Explainability of Machine Learning in Work Disability Risk Prediction

Published:2023 Issue: Volume: Page:236-245
ISSN:2367-3370
Container-title:Proceedings of the 2023 International Conference on Advances in Computing Research (ACR’23)
language:
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Author:

Saarela Katja,Huhta-Koivisto Vili,Kemell Kai-Kristian,Nurminen Jukka K.

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-33743-7_20

Reference28 articles.

1. OECD: Sickness, Disability and Work: Breaking the Barriers. OECD Publishing, Paris (2010)

2. Saarela, K., Huhta-Koivisto, V., Nurminen, J.K.: Ethical aspects of work disability risk prediction using machine learning. In: Nagar, A.K., Singh Jat, D., Kumar Mishra, D., Joshi, A. (eds.) Intelligent Sustainable Systems: Selected Papers of WorldS4 2022, vo. 1, pp. 499–509. Springer Nature, Singapore (2023). https://doi.org/10.1007/978-981-19-7660-5_43

3. Airaksinen, J., et al.: Development and validation of a risk prediction model for work disability: multicohort study. Sci. Rep. 7(1), 13578 (2017)

4. Salonen, L., Blomgren, J., Laaksonen, M.: From long-term sickness absence to disability retirement: diagnostic and occupational class differences within the working-age Finnish population. BMC Publ. Health 20(1), 1–11 (2020)

5. Salonen, L., Alexanderson, K., Rugulies, R., et al.: Combinations of job demands and job control and future trajectories of sickness absence and disability pension an 11-year follow-up of two million employees in Sweden. J. Occup. Env. Med. 62(10), 795–802 (2020)

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1. Work Disability Risk Prediction Using Machine Learning;Current and Future Trends in Health and Medical Informatics;2023

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