用床垫下信号预测痴呆患者次日躁动风险，保留分钟级时序更有效。
Under-Mattress Temporal Sensing for Next-Day Agitation Risk Scoring in Dementia Wards
- 基于床垫传感器的整夜信号序列建模，捕捉分钟级动态变化
- 模型在次日躁动预测中达到0.692的AUROC和0.849的AUPRC
- 适合关注痴呆患者非接触式生理监测与智能预警的研究者
痴呆患者的躁动症状在短时间尺度上波动剧烈，但用于预测次日风险的连续生理数据仍有限。本研究评估了前一晚床垫下的无接触传感信号是否可预测次日躁动风险，并考察保留分钟级时序结构是否优于传统夜间摘要。分析了65名患者共423个患者-夜晚的数据，使用两种床垫传感系统。构建了一个统一的四范式基准，比较了手工提取的夜间摘要、三时段特征、全夜序列建模及滑动窗口多实例学习方法。采用源特定预处理和五折患者分组交叉验证，通过汇总留出预测评估性能。评估包括区分度、校准性、固定阈值指标，以及两种时序模型的周期信号归因模式对比。全夜序列建模在区分度（AUROC 0.692；AUPRC 0.849）和平衡准确率（0.658）上表现最佳。两种分钟级方法的AUROC均高于夜间摘要，但与三时段手工特征的差异不确定。跨模型归因显示核心过夜期的活动、心率和呼吸率最重要。校准性仍受限。前一晚的信号支持适度的次日风险区分，分钟级时序建模优于夜间摘要。需前瞻性校准与外部验证后方可用于个体照护决策。该患者分组基准确认无接触夜间传感是住院痴呆队列躁动风险研究的有前景方向。
原文摘要 · Abstract (English)
Agitation fluctuates over short time horizons in people living with dementia, yet continuous physiological information for anticipating next-day risk is limited. We assessed whether contactless under-mattress signals from the preceding night inform next-day agitation risk and whether preserving minute-level temporal structure improves performance over conventional nightly summaries. We analyzed 423 patient-nights from 65 subjects in a specialized hospital dementia unit using two under-mattress sensing systems. A unified four-paradigm benchmark compared nightly handcrafted summaries, three-period handcrafted features, full-night sequence modeling, and sliding-window multiple-instance learning. Source-specific preprocessing and five-fold patient-grouped cross-validation were used, with performance estimated from pooled out-of-fold predictions. Evaluation included discrimination, calibration, fixed-threshold metrics, and a comparison of period-signal attribution patterns across two temporal models. Full-night sequence modeling achieved the highest discrimination (AUROC, 0.692; AUPRC, 0.849) and balanced accuracy (0.658). Both minute-level pipelines had higher AUROC than nightly summaries, but differences from three-period handcrafted features were uncertain. Cross-model attribution prioritized activity, heart rate, and respiratory rate during the core overnight period. Calibration remained limited. The preceding night's signals supported modest next-day risk discrimination, with minute-level temporal modeling outperforming nightly summaries. Prospective calibration and external validation are needed before use in individual care decisions. This patient-grouped benchmark identifies contactless overnight sensing as a promising biomedical engineering direction for agitation-risk research in hospitalized dementia cohorts.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。