Using machine learning to identify relevant features for the diagnosis of chronic back pain
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| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-01-01
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| Series: | Brain and Spine |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772529424013754 |
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| _version_ | 1850220741659721728 |
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| author | S. Vickery F. Junker R. Döding D. Belavy M. Angelova C. Karmakar L.A. Becker N. Taheri M. Pumberger S. Reitmaier H. Schmidt |
| author_facet | S. Vickery F. Junker R. Döding D. Belavy M. Angelova C. Karmakar L.A. Becker N. Taheri M. Pumberger S. Reitmaier H. Schmidt |
| author_sort | S. Vickery |
| collection | DOAJ |
| format | Article |
| id | doaj-art-14ca0107eef54a2aa756732b1e76051a |
| institution | OA Journals |
| issn | 2772-5294 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Brain and Spine |
| spelling | doaj-art-14ca0107eef54a2aa756732b1e76051a2025-08-20T02:06:57ZengElsevierBrain and Spine2772-52942024-01-01410411910.1016/j.bas.2024.104119Using machine learning to identify relevant features for the diagnosis of chronic back painS. Vickery0F. Junker1R. Döding2D. Belavy3M. Angelova4C. Karmakar5L.A. Becker6N. Taheri7M. Pumberger8S. Reitmaier9H. Schmidt10Hochschule für Gesundheit, Department für Angewandte Gesundheitswissenschaften, Bochum, DeutschlandHochschule für Gesundheit, Department für Angewandte Gesundheitswissenschaften, Bochum, DeutschlandHochschule für Gesundheit, Department für Angewandte Gesundheitswissenschaften, Bochum, DeutschlandHochschule für Gesundheit, Department für Angewandte Gesundheitswissenschaften, Bochum, DeutschlandAston University, Aston Digital Futures Institute, Birmingham, Vereinigtes KönigreichDeakin University, School of Information Technology, Melbourne, AustralienCharité – Universitätsmedizin Berlin, Centrum für Muskuloskeletale Chirurgie, Berlin, Deutschland; Charité – Universitätsmedizin Berlin, Julius Wolff Institut, Berlin Institute of Health, Berlin, DeutschlandCharité – Universitätsmedizin Berlin, Centrum für Muskuloskeletale Chirurgie, Berlin, Deutschland; Charité – Universitätsmedizin Berlin, Julius Wolff Institut, Berlin Institute of Health, Berlin, DeutschlandCharité – Universitätsmedizin Berlin, Centrum für Muskuloskeletale Chirurgie, Berlin, DeutschlandCharité – Universitätsmedizin Berlin, Julius Wolff Institut, Berlin Institute of Health, Berlin, DeutschlandCharité – Universitätsmedizin Berlin, Julius Wolff Institut, Berlin Institute of Health, Berlin, Deutschlandhttp://www.sciencedirect.com/science/article/pii/S2772529424013754 |
| spellingShingle | S. Vickery F. Junker R. Döding D. Belavy M. Angelova C. Karmakar L.A. Becker N. Taheri M. Pumberger S. Reitmaier H. Schmidt Using machine learning to identify relevant features for the diagnosis of chronic back pain Brain and Spine |
| title | Using machine learning to identify relevant features for the diagnosis of chronic back pain |
| title_full | Using machine learning to identify relevant features for the diagnosis of chronic back pain |
| title_fullStr | Using machine learning to identify relevant features for the diagnosis of chronic back pain |
| title_full_unstemmed | Using machine learning to identify relevant features for the diagnosis of chronic back pain |
| title_short | Using machine learning to identify relevant features for the diagnosis of chronic back pain |
| title_sort | using machine learning to identify relevant features for the diagnosis of chronic back pain |
| url | http://www.sciencedirect.com/science/article/pii/S2772529424013754 |
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