English(EN) Predicting Multiple Clinical Outcomes Related to Functional Recovery and Social Isolation Among Older Adults After Lower-Limb Fracture or Hip Replacement

AI模型利用传感器数据预测老年人的恢复结局

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-25 04:00

研究人员开发了一个多输出回归模型NODE，用于预测老年人下肢骨折或髋关节置换术后恢复的多种临床结局。该研究利用了MAISON-LLF数据集，整合了八周内的多模态传感器数据和临床评估。通过联合预测社会隔离、功能恢复评分和活动能力测试等结局，该模型实现了3.96的均方误差和1.02的平均绝对误差，优于单输出模型。使用SHAP进行的特征分析强调了多模态传感器在准确估计患者恢复轨迹中的重要性。 AI

影响这项研究展示了AI通过提供更全面的恢复评估来改善老年人个性化护理和生活质量的潜力。

排序理由学术论文，详细介绍了用于临床结局预测的新型机器学习模型。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.CV TIER_1 English(EN) · Santosh Ray, Pratik K. Mishra, Ali Abedi, Charlene H. Chu, Amir Ahmad, Shehroz S. Khan · 2026-08-25 04:00

预测老年人下肢骨折或髋关节置换术后功能恢复与社会隔离相关的多种临床结局

arXiv:2608.23531v1 Announce Type: new Abstract: Older adults recovering after lower-limb fracture or hip replacement may experience complex recovery trajectories. Most of the time, these clinical aspects are studied in isolation, masking their joint impact on recovery. This study…

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报道来源 [1]

预测老年人下肢骨折或髋关节置换术后功能恢复与社会隔离相关的多种临床结局

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