发表机构
Liwa University; University of Toronto; University Health Network; United Arab Emirates University; American University of the Middle East（利瓦大学; 多伦多大学; 大学健康网络; 阿联酋大学; 中东美国大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究基于MAISON-LLF数据集构建多输出回归模型，用NODE算法联合预测下肢骨折或髋关节置换术后老年人的5项临床结局，效果优于单输出模型，可辅助评估其功能恢复与社交参与。
AI 中文摘要
下肢骨折或髋关节置换术后恢复的老年人可能会经历复杂的恢复轨迹，多数情况下这些临床方面被单独研究，掩盖了它们对恢复的联合影响。本研究使用MAISON-LLF数据集，该数据集包含18名社区中下肢骨折或髋关节置换术后恢复的老年人的多模态传感器和临床评估数据，参与者接受了长达8周的监测，对应最多1008个参与者日的传感器监测数据。从室内运动、加速度、步数、心率、外出活动和睡眠数据中提取了46个每日特征，每两周评估一次5项临床结局：社交孤立量表、牛津髋关节评分、牛津膝关节评分、计时起立行走测试和30秒坐立测试。我们利用多模态传感器数据与不同临床评分之间的内在关系，将其构建为多输出回归问题，测试了多种机器学习和深度学习的单输出及多输出回归算法来同时预测这些评分。结果显示，联合预测临床评分比单独预测效果更好；表格型深度学习多输出回归模型NODE表现出色，均方误差（MSE）为3.96，平均绝对误差（MAE）为1.02，优于其他多输出和单输出回归模型。SHAP特征分析进一步表明，纳入多模态传感器数据对准确估计患者恢复轨迹至关重要。本研究或可支持对社区居住老年人的功能恢复和社交参与进行同步评估，最终有助于改善其护理和生活质量。
英文摘要
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 used the MAISON-LLF dataset, which contains multimodal sensor and clinical assessment data from 18 older adults recovering in the community after lower-limb fracture or hip replacement. Participants were monitored for up to eight weeks, corresponding to a maximum of 1,008 participant-days of sensor monitoring. Forty-six daily features were extracted from indoor motion, acceleration, step count, heart rate, out-of-home mobility, and sleep data. Five clinical outcomes were assessed every two weeks: the Social Isolation Scale, Oxford Hip Score, Oxford Knee Score, Timed Up and Go test, and 30-second Chair Stand test. We utilize an inherent relationship between multi-modal sensor data and different clinical scores and formulate it as a multi-output regression problem. We tested various machine learning and deep learning single- and multi-output regression algorithms to predict these scores simultaneously. The results showed that predicting clinical scores jointly was better than separately. The tabular DL multi-output regressor, NODE, gave a remarkable performance of MSE=3.96 and MAE=1.02 in comparison to other multi- and single-output regressors. The SHAP feature analysis further showed the importance of including multimodal sensors to provide a good estimate of patients' recovery trajectory. This work may support the simultaneous assessment of functional recovery and social engagement among community-dwelling older adults and ultimately help improve their care and quality of life.
