I JMIR出版物一种无监督数据驱动的异常检测方法用于痴呆患者的不良健卡塔尔世界杯8强波胆分析康状况:队列研究%A Bijlani,Nivedita %A Nilforooshan,Ramin %A Kouchaki,Samaneh %+萨里大学视觉、语音和信号处理中心，格尔福德388 Stag Hill, GU2 7XH，英国，44 1483 300 800,n.bijlani@surrey.ac.uk %K情境矩阵profile %K多维异常检测%K异常值检测%K基于传感器的远程健康监测%K痴呆%K无监督学习%D 2022 %7 19.9.2022 %9原始论文%J JMIR老化%G英语%X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，对他们的日常生活影响最小。异常检测方法已广泛应用于各个领域，包括远程健康监测。然而，目前的方法受到噪声、多变量数据和低泛化能力的挑战。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月期间英国痴呆研究所从15个参与家庭收集的9363天真实数据集上，证明了它比最先进方法的有效性。我们的方法应用于家庭移动数据，以检测尿路感染(UTIs)和住院情况。方法:提出并评估一种基于上下文矩阵剖面(CMP)的精确、超快距离异常检测算法。使用通过被动红外传感器收集的每日家庭运动数据，我们生成了基于位置的传感器计数、持续时间和每个患者每小时运动模式变化的cmp。 We computed a normalized anomaly score in 2 ways: by combining univariate CMPs and by developing a multidimensional 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. %M 36121687 %R 10.2196/38211 %U https://aging.www.mybigtv.com/2022/3/e38211 %U https://doi.org/10.2196/38211 %U http://www.ncbi.nlm.nih.gov/pubmed/36121687