Efficacy of a machine learning model integrating clinical-psychosocial factors in predicting posttraumatic bone nonunion and bone defects

doi:10.5498/wjp.119455

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World Journal of Psychiatry

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2220-3206

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

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Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.

World J Psychiatry. Sep 19, 2026; 16(9): 119455
Published online Sep 19, 2026. doi: 10.5498/wjp.119455

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Figure 2 Bar chart showing SHapley Additive exPlanations global feature importance based on the external validation cohort. A: The traditional clinical model; B: The integrated model. BMI: Body mass index; SHAP: SHapley Additive exPlanations; PHQ-9: Patient Health Questionnaire-9; GAD-7: Generalized Anxiety Disorder-7; MSPSS: Multidimensional Scale of Perceived Social Support.

Citation: Luan FF, Chen JY, Zheng YZ, Hu MH, Huang F, Zheng CX. Efficacy of a machine learning model integrating clinical-psychosocial factors in predicting posttraumatic bone nonunion and bone defects. World J Psychiatry 2026; 16(9): 119455
URL: https://www.wjgnet.com/2220-3206/full/v16/i9/119455.htm
DOI: https://dx.doi.org/10.5498/wjp.119455

Luan FF, Chen JY, Zheng YZ, Hu MH, Huang F, Zheng CX. Efficacy of a machine learning model integrating clinical-psychosocial factors in predicting posttraumatic bone nonunion and bone defects. World J Psychiatry 2026; 16(9): 119455 [DOI: 10.5498/wjp.119455]

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