Immune cell profiles and predictive modeling in osteoporotic vertebral fractures using XGBoost machine learning algorithms

Background Osteoporosis significantly increases the risk of vertebral fractures, particularly among postmenopausal women, decreasing their quality of life. These fractures, often undiagnosed, can lead to severe health consequences and are influenced by bone mineral density and abnormal loads. Manage...

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Veröffentlicht in:	BioData mining Jg. 18; H. 1; S. 13 - 20
Hauptverfasser:	Chen, Yi-Chou, Su, Hui-Chen, Huang, Shih-Ming, Yu, Ching-Hsiao, Chang, Jen-Huei, Chiu, Yi-Lin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London BioMed Central 04.02.2025 BioMed Central Ltd Springer Nature B.V BMC
Schlagworte:	Algorithms B cells Bioinformatics Biomarkers Biomedical and Life Sciences Bone density Bone growth Bone healing Bone mineral density Bone resorption Bones CD4 antigen Cell differentiation Cells Computational Biology/Bioinformatics Computer Appl. in Life Sciences Cytokines Data mining Data Mining and Knowledge Discovery Data Mining in Biomedical informatics and Healthcare Datasets Fractures Gene expression Genes Helper cells Hematopoietic stem cells Homeopathy Immune response Immune system Immunological memory Immunology Life Sciences Lymphocytes Lymphocytes T Machine learning Materia medica and therapeutics Memory cells Osteoclastogenesis Osteogenesis Osteoporosis Pathogenesis Post-menopause Postmenopausal women Prediction models Quality of life Spinal cord Stem cells Strategic planning (Business) T cells Th17 cell differentiation Therapeutic targets Therapeutics Vertebrae Vertebral fractures Womens health XGBoost Osteoporosis Vertebral fractures Th17 cell differentiation XGBoost
ISSN:	1756-0381, 1756-0381
Online-Zugang:	Volltext
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