Predicting pregnancy-related pelvic girdle pain using machine learning.
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| Title: | Predicting pregnancy-related pelvic girdle pain using machine learning. |
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| Authors: | Ashrafi A; School of Health Sciences, Western Sydney University, Sydney, New South Wales, Australia. Electronic address: a.ashrafi@westernsydney.edu.au., Thomson D; School of Health Sciences, Western Sydney University, Sydney, New South Wales, Australia., Khorshidi HA; School of Computing and Information Systems, The University of Melbourne, Parkville, Victoria, Australia; School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia., Marashi A; School of Medical Science, Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia., Beales D; School of Allied Health, Faculty of Health Sciences, Curtin University, Perth, WA, Australia; enAble Institute, Faculty of Health Sciences, Curtin University, Perth, WA, Australia., Ceprnja D; Department of Physiotherapy, Westmead Hospital, Sydney, New South Wales, Australia; School of Science and Health, Western Sydney University, Sydney, New South Wales, Australia., Gupta A; School of Health Sciences, Western Sydney University, Sydney, New South Wales, Australia; Department of Physiotherapy, Westmead Hospital, Sydney, New South Wales, Australia. |
| Source: | Musculoskeletal science & practice [Musculoskelet Sci Pract] 2025 Jun; Vol. 77, pp. 103321. Date of Electronic Publication: 2025 Mar 25. |
| Publication Type: | Journal Article; Research Support, Non-U.S. Gov't |
| Journal Info: | Publisher: Elsevier Country of Publication: Netherlands NLM ID: 101692753 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2468-7812 (Electronic) Linking ISSN: 24687812 NLM ISO Abbreviation: Musculoskelet Sci Pract Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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