@文章{信息:doi/10.2196/38211，作者=“Bijlani, Nivedita和Nilforooshan, Ramin和Kouchaki, Samaneh”，标题=“痴呆症患者不良健康状况的无监督数据驱动异常检测方法:队列研究”，期刊=“JMIR老化”，年=“2022”，月=“Sep”，日=“19”，卷=“5”，数=“3”，页=“e38211”，关键词=“情境矩阵简介;多维异常检测;异常检测;基于传感器的远程健康监测;老年痴呆症;背景:基于传感器的远程健康监测可用于及时检测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。 Our approach was applied to household movement data to detect urinary tract infections (UTIs) and hospitalizations. Methods: We propose and evaluate a solution based on Contextual Matrix Profile (CMP), an exact, ultrafast distance-based anomaly detection algorithm. Using daily aggregated household movement data collected via passive infrared sensors, we generated CMPs for location-wise sensor counts, duration, and change in hourly movement patterns for each patient. 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. ", issn="2561-7605", doi="10.2196/38211", url="https://aging.www.mybigtv.com/2022/3/e38211", url="https://doi.org/10.2196/38211", url="http://www.ncbi.nlm.nih.gov/pubmed/36121687" }