An Unsupervised Data-Driven Anomaly Detection Approach for Adverse Health Conditions in People Living With Dementia: Cohort Study

Abstract

Background Sensor-based remote health monitoring can be used for the timely detection of health deterioration in people living with dementia with minimal impact on their day-to-day living. Anomaly detection approaches have been widely applied in various domains, including remote health monitoring. However, current approaches are challenged by noisy, multivariate data and low generalizability. Objective This study aims to develop an online, lightweight unsupervised learning–based approach to detect anomalies representing adverse health conditions using activity changes in people living with dementia. We demonstrated its effectiveness over state-of-the-art methods on a real-world data set of 9363 days collected from 15 participant households by the UK Dementia Research Institute between August 2019 and July 2021. 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.

Early warning systems for malaria outbreaks in Thailand: an anomaly detection approach

“…Similarly, the use of unsupervised anomaly detection methods have been used to discover implausible electronic health records in cancer registries [ 19 ] and adverse health conditions for people living dementia using sensor-base data [ 20 ]. In a study exploring the use of unsupervised anomaly detection for disease surveillance, Brazilian Amazon malaria surveillance data is used as a case study for early detection of outbreaks [ 21 ].…”

Artificial intelligence for dementia prevention

“…Clustering methods such as multi‐layer clustering, k‐means clustering, hierarchical clustering, or Gaussian mixture models (GMMs) are unsupervised methods that have been applied in dementia prevention research, for example, to identify subgroups of individuals with MCI with markedly different prognostic cognitive trajectories 16 The results from such studies can be used to identify subgroups of individuals that are at high risk of dementia to better understand different disease trajectories and tailor interventions or prevention strategies accordingly 17 . Unsupervised anomaly detection algorithms, including one‐class support vector machines (SVMs), autoencoders (AEs), or isolation forests, have been utilized to identify unusual or atypical patterns in data 18,19 . In the context of dementia prevention, these methods could help detect outliers or abnormal biomarker patterns that may be indicative of underlying pathology or increased risk 18 .…”

“…Unsupervised anomaly detection algorithms, including one‐class support vector machines (SVMs), autoencoders (AEs), or isolation forests, have been utilized to identify unusual or atypical patterns in data 18,19 . In the context of dementia prevention, these methods could help detect outliers or abnormal biomarker patterns that may be indicative of underlying pathology or increased risk 18 . Other unsupervised methods include unsupervised latent variable models, such as latent Dirichlet allocation (LDA) or GMMs, which have been employed to identify latent structures or hidden variables in data, which can reveal hidden patterns or subgroup data sets that may be relevant for understanding dementia risk factors or disease progression 20,21 …”

Integrating Unsupervised Machine Intelligence and Anomaly Detection for Spatio-Temporal Dynamic Mapping Using Remote Sensing Image Series

“…Anomaly detection (AD) comprises a category of unsupervised machine learning that aims at identifying elements that do not follow an expected behavior [15]. Several studies in the scientific literature have been supported by anomaly detection methods, which include recent applications in domains such as health sciences [16], social monitoring [17], and psychology [18]. Furthermore, anomaly detection methods also appear in the context of remote sensing, e.g., to detect temporal changes on the Earth's surface [19][20][21].…”