用大模型解析老人骨折康复的多模态传感器数据，自动识别风险轨迹。
Explaining Recovery Trajectories of Older Adults Post Lower-Limb Fracture Using Modality-wise Multiview Clustering and Large Language Models
- 分模态聚类+大模型提示，自动给康复数据打有意义标签。
- 90%以上聚类标签与临床评分显著相关，验证方法有效。
- 适合临床医生快速筛查高风险患者，无需标注数据。
在缺乏标注的情况下，如何解释高维、海量的医疗数据仍是重大挑战。本文针对社区中老年患者下肢骨折康复期间采集的多模态传感器数据（共560天），开展无监督分析。数据包括加速度、步数、环境运动、GPS位置、心率和睡眠等六类传感器信号，以及同步收集的临床评分。首先对每种数据模态分别进行聚类，评估不同特征集对康复轨迹的影响；随后通过上下文感知提示，利用大语言模型为各模态聚类结果生成可理解的标签。通过严格的统计检验和可视化对比临床评分，验证了多数由大模型生成的模态特异性聚类标签具有显著性。结果表明，该方法仅依赖传感器数据即可有效揭示康复模式，帮助临床医生及时识别高风险患者，改善健康结局。
原文摘要 · Abstract (English)
Interpreting large volumes of high-dimensional, unlabeled data in a manner that is comprehensible to humans remains a significant challenge across various domains. In unsupervised healthcare data analysis, interpreting clustered data can offer meaningful insights into patients' health outcomes, which hold direct implications for healthcare providers. This paper addresses the problem of interpreting clustered sensor data collected from older adult patients recovering from lower-limb fractures in the community. A total of 560 days of multimodal sensor data, including acceleration, step count, ambient motion, GPS location, heart rate, and sleep, alongside clinical scores, were remotely collected from patients at home. Clustering was first carried out separately for each data modality to assess the impact of feature sets extracted from each modality on patients' recovery trajectories. Then, using context-aware prompting, a large language model was employed to infer meaningful cluster labels for the clusters derived from each modality. The quality of these clusters and their corresponding labels was validated through rigorous statistical testing and visualization against clinical scores collected alongside the multimodal sensor data. The results demonstrated the statistical significance of most modality-specific cluster labels generated by the large language model with respect to clinical scores, confirming the efficacy of the proposed method for interpreting sensor data in an unsupervised manner. This unsupervised data analysis approach, relying solely on sensor data, enables clinicians to identify at-risk patients and take timely measures to improve health outcomes.
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