Comparing three machine learning approaches to design a risk assessment tool for future fractures: predicting a subsequent major osteoporotic fracture in fracture patients with osteopenia and osteoporosis-Reference-Cited by-同舟云学术

Comparing three machine learning approaches to design a risk assessment tool for future fractures: predicting a subsequent major osteoporotic fracture in fracture patients with osteopenia and osteoporosis

Published:2021-01-07 Issue:3 Volume:32 Page:437-449
ISSN:0937-941X
Container-title:Osteoporosis International
language:en
Short-container-title:Osteoporos Int

Author:

de Vries B. C. S.^ORCID,Hegeman J. H.^ORCID,Nijmeijer W.^ORCID,Geerdink J.^ORCID,Seifert C.^ORCID,Groothuis-Oudshoorn C. G. M.^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Endocrinology, Diabetes and Metabolism

Link

http://link.springer.com/content/pdf/10.1007/s00198-020-05735-z.pdf

Reference41 articles.

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2. Johnell O, Kanis JA (2006) An estimate of the worldwide prevalence and disability associated with osteoporotic fractures. Osteoporos Int 17:1726–1733. https://doi.org/10.1007/s00198-006-0172-4

3. Briot K, Paternotte S, Kolta S, Eastell R, Felsenberg D, Reid DM, Glüer CC, Roux C (2013) FRAX®: prediction of major osteoporotic fractures in women from the general population: the OPUS study. PLoS One 8:e83436. https://doi.org/10.1371/journal.pone.0083436

4. Jürisson M, Pisarev H, Kanis J, Borgström F, Svedbom A, Kallikorm R, Lember M, Uusküla A (2016) Quality of life, resource use, and costs related to hip fracture in Estonia. Osteoporos Int 27:2555–2566. https://doi.org/10.1007/s00198-016-3544-4

5. Goldacre MJ, Roberts SE, Yeates D (2002) Mortality after admission to hospital with fractured neck of femur: database study. BMJ 325:868–869. https://doi.org/10.1136/bmj.325.7369.868

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