Doctoral Dissertations

Orcid ID

Date of Award


Degree Type


Degree Name

Doctor of Philosophy


Computer Engineering

Major Professor

Michael A. Langston

Committee Members

Olufemi A Omitaomu, David J. Icove, Catherine D. Schuman


While modern complex computer systems provide enormous benefits to our daily lives, the increasing complexity of these large-scale systems also makes them more susceptible to unexpected software malfunctions and malicious attacks. This is especially true for Health Information Technology (HIT), which has revolutionized healthcare delivery by making it more efficient, effective, and accessible. Nevertheless, the widespread adoption of HIT has introduced new challenges related to ensuring system reliability and security. As a result, the development of novel algorithms and frameworks to detect anomalies in such systems has become increasingly important for enhancing patient safety and improving the efficiency and effectiveness of healthcare services.

This dissertation presents innovative approaches for anomaly detection in HIT systems using Electronic Health Records (EHR), addressing the complexity of HIT and the need for patient data privacy and security. The first approach is an event sequence and subsequence anomaly detection algorithm that utilizes network-based representations, considers higher-order dependencies, and incorporates salient information of sequences for discrimination. By monitoring changes in the graph structure after removing test sequences, the algorithm effectively identifies anomalies and suggests plausible transitions for detected anomalous subsequences. Leveraging the cutting-edge natural language processing model Bidirectional Encoder Representations from Transformers (BERT), the second approach, named EHR-BERT, learns and identifies patterns in sequences bidirectionally, demonstrating improved accuracy and reduced false negatives through extensive evaluations. The third approach presents a comprehensive framework for detecting system-level abnormal events using high-dimensional system volume data. This framework employs machine learning models to identify outliers through a voting machine strategy, constructs weighted graphs representing the correlation of co-occurring outliers, and utilizes spectral graph theory and the paraclique algorithm to capture clusters with high correlations of co-occurring anomalies.

Collectively, the proposed approaches advance state-of-the-art in anomaly detection in HIT systems and contribute to the development of advanced algorithms that can be applied in various clinical domains, ultimately improving patient outcomes and enhancing the reliability and efficiency of healthcare services. Additionally, the proposed approaches have the potential to be applied to other complex computer systems beyond HIT, where anomaly detection is critical for maintaining system reliability and security.

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