title: Empowering health data insights
title: tools for anomaly detection and multivariate clustering
titleType: Subtitle
Creators: name: Kuwaye, Daren
Publisher: name: University of Iowa
Year: 2025
Subjects: Anomaly detection
Functional data analysis
Joint modeling
Longitudinal clustering
Model-based proximity

description: Medical records have a long history; ancient stone tablets containing patient medical history have been unearthed. In modern times, patient data are more easily recorded in forms such as electronic medical records (EMR) and wearable device data. These efficient systems quickly create large datasets filled with latent features waiting to be discovered. To uncover these insights, clustering techniques can be used to identify patients with similar characteristics. However, these data have immutable properties that available techniques inadequately address. Specifically, wearable device data contain multiple responses that are highly correlated and EMRs tend to be recorded at inconsistent intervals. Furthermore, since the clustering results can have wide-ranging implications through guiding medical treatments, resource allocation and healthcare policy, ensuring data quality is of paramount importance. However in EMRs, data are typically measured and entered into the record by hand. This human element intensifies the risk of contaminating the data with anomalies. Thus, this dissertation introduces three tools to address the issues of anomaly detection and clustering.
description: In the second chapter, we provide a methodology for identifying and rectifying anomalies in EMR data. Rooted in a mean-shift model and utilizing studentized residuals, our approach is carefully crafted to adopt a conservative stance, specifically targeting anomalies that indicate mistakes in the data collection process. We incorporate strict control over the false discovery rate to bolster the dependability of identified anomalies. We substantiate our approach by presenting extensive simulation studies and a real-world application, affirming its capacity not only to pinpoint anomalies but also to facilitate precise data correction. Thus, it serves as an invaluable tool for analysts seeking to enhance data quality and reliability.
description: We then present a technique for clustering unaligned EMRs in the third chapter. We employ smoothing splines to model individual trends and introduce a model-based proximity, which is derived from the covariance matrices of fitted smoothing spline models. This approach ensures a sophisticated capture of subject-specific correlations, thereby enhancing the accuracy of clustering. By leveraging the k-medoids algorithm with this refined proximity, we achieve precise grouping of subjects who exhibit similar trends over time. Our methodology is validated through extensive simulation studies, highlighting its superior performance when compared to existing techniques. We further demonstrate the practical application and effectiveness of this approach through realworld case studies involving both EMR and wearable device data.
description: The fourth chapter tackles the challenge of addressing the highly correlated channels present in wearable device data. We introduce an innovative approach that employs a joint regression model to effectively characterize and integrate the correlations among the multiple channels over time. Utilizing B-splines, we approximate functional trends and calculate pairwise proximities using a model-based covariance matrix. This proximity, when combined with the k-medoids algorithm, streamlines the clustering of subjects into groups that exhibit consistent trends across various channels. Our method’s superiority in cases with substantial correlations, where conventional techniques struggle, is demonstrated through extensive simulation studies. The practical utility and enhanced accuracy of this approach are exemplified in an electroencephalography study.