TY -的盟Bijlani Nivedita盟——Nilforooshan白木盟——Kouchaki Samaneh PY - 2022 DA - 2022/9/19 TI -一个无监督数据驱动的异常检测方法痴呆患者的不良健康状况:队列研究乔- JMIR老化SP - e38211六世- 5 - 3 KW -上下文矩阵概要KW -多维异常检测KW -孤立点检测千瓦传感器远程健康监测KW -痴呆KW -无监督学习AB -背景:基于传感器的远程健康监测可用于及时发现痴呆症患者的健康恶化，对其日常生活的影响最小。异常检测方法已广泛应用于各个领域，包括远程健康监控。然而，目前的方法受到噪声、多变量数据和低泛化的挑战。目的:本研究旨在开发一种基于在线、轻量级无监督学习的方法，利用痴呆症患者的活动变化来检测代表不良健康状况的异常。我们在2019年8月至2021年7月期间，英国痴呆症研究所从15个参与家庭收集了9363天的真实数据集，证明了其优于最先进方法的有效性。我们的方法应用于家庭运动数据，以检测尿路感染(UTIs)和住院情况。方法:我们提出并评估了一种基于上下文矩阵轮廓(CMP)的解决方案，这是一种精确、超快速的基于距离的异常检测算法。利用被动红外传感器收集的每日汇总的家庭运动数据，我们生成了每个患者的位置传感器计数、持续时间和每小时运动模式变化的cmp。我们通过两种方式计算了标准化异常评分:结合单变量CMP和开发多维CMP。 The performance of our method was evaluated relative to Angle-Based Outlier Detection, Copula-Based Outlier Detection, and Lightweight Online Detector of Anomalies. We used the multidimensional CMP to discover and present the important features associated with adverse health conditions in people living with dementia. Results: The multidimensional CMP yielded, on average, 84.3% recall with 32.1 alerts, or a 5.1% alert rate, offering the best balance of recall and relative precision compared with Copula-Based and Angle-Based Outlier Detection and Lightweight Online Detector of Anomalies when evaluated for UTI and hospitalization. Midnight to 6 AM bathroom activity was shown to be the most important cross-patient digital biomarker of anomalies indicative of UTI, contributing approximately 30% to the anomaly score. We also demonstrated how CMP-based anomaly scoring can be used for a cross-patient view of anomaly patterns. Conclusions: To the best of our knowledge, this is the first real-world study to adapt the CMP to continuous anomaly detection in a health care scenario. The CMP inherits the speed, accuracy, and simplicity of the Matrix Profile, providing configurability, the ability to denoise and detect patterns, and explainability to clinical practitioners. We addressed the need for anomaly scoring in multivariate time series health care data by developing the multidimensional CMP. With high sensitivity, a low alert rate, better overall performance than state-of-the-art methods, and the ability to discover digital biomarkers of anomalies, the CMP is a clinically meaningful unsupervised anomaly detection technique extensible to multimodal data for dementia and other health care scenarios. SN - 2561-7605 UR - https://aging.www.mybigtv.com/2022/3/e38211 UR - https://doi.org/10.2196/38211 UR - http://www.ncbi.nlm.nih.gov/pubmed/36121687 DO - 10.2196/38211 ID - info:doi/10.2196/38211 ER -