Biomedical Informatics Commons^™

Essays On Ai-Driven Analytics For Enhanced Healthcare Delivery: From Onset To Outcomes In Neurological Diseases Management, Gabriel Owusu

Theses and Dissertations

Chronic neurological diseases, particularly Alzheimer's disease and related dementias (ADRDs), pose mounting global healthcare burdens. Despite advances in AI and health information technologies, early detection, precise diagnosis, and effective caregiver support remain elusive. This dissertation proposes innovative AI-enabled analytical frameworks to address these challenges across the full ADRD continuum.

The first essay designs an explainable AI-enabled clinical decision support system (CDSS) using a graph neural network for early AD identification, evaluated through usability studies demonstrating improved risk awareness and clinical engagement. The second essay introduces MADT-RNN, a multi-level attention-based deep transfer recurrent neural network that fuses convolutional and recurrent architectures …

Uncovering Potential Therapeutic Targets For Spinal Cord Injury; Insights From Single-Cell And Cross-Species Transcriptomics, Sara Samy Abouzekry

Theses and Dissertations

Central nervous system (CNS) injury remains a devastating healthcare problem. Despite years of research, it remains almost impossible to achieve meaningful functional repair following traumatic brain injury (TBI) and spinal cord injury (SCI). Spinal cord injury (SCI), a highly devastating subset of CNS trauma, is considered a challenge due to the extremely limited regenerative capacity of the nervous system in adult mammals. In contrast, certain species display remarkable repair abilities followed by moderate regenerative ability in neonatal mammals, indicating that regeneration is shaped by the evolutionary context as well as the developmental age.

To uncover the core molecular mediators of …

Metabolic Syndrome Beyond Diagnostic Criteria: Population-Scale Integrative Metabolomics Characterization, Marwa Talal

Theses and Dissertations

Background: Metabolic syndrome (MetS) is a complex cluster of interrelated metabolic abnormalities associated with elevated cardiometabolic risk. While diagnosis is based on well-established five clinical criteria, these may overlook early or atypical metabolic alterations. Large-scale metabolomic profiling offers an opportunity to identify biochemical signatures of MetS beyond diagnostic bias and to evaluate their relative importance across different presentations of the syndrome.

Methods: Data from 117,147 UK Biobank participants were analyzed in a cross-sectional design. High-throughput NMR quantified 75 circulating metabolites, for. Univariate analyses, MetS subtype stratification, and elastic net models with SHAP interpretation were applied to assess feature …

A Picture Tells A Thousand Words—, But Ecg Signals Have More To Say, Ashley N. Gomez

Theses and Dissertations

With the increasing adoption of deep learning classification models in the medical domain, a critical challenge remains: achieving high predictive accuracy while maintaining clinical Inter-pretability. This study examines how model architecture, dataset origin, and the use of full versus subset data affect both classification performance and Interpretability in Electrocardiogram (ECG) signal analysis. ResNet18 is evaluated using an open-source ECG Image Dataset, thus a custom dataset derived from digitized ECG images. Post-hoc explainability methods, such as Integrated Gradients, are applied to determine which time steps have the most significant influence on model decisions. The findings demonstrate that model architecture and dataset …

Enhancing Clinical Trial Matching In Molecular Diagnostics: Using Natural Language Processing And Clustering Approaches In Hematological Malignancies, Gillian Fanning

Theses and Dissertations

Clinical trial matching is a critical component of personalized medicine, particularly in the management of hematologic malignancies. At Virginia Commonwealth University (VCU) Health, the Molecular Diagnostics (MDX) Lab produces somatic variant reports and recommends clinical trials based on the presence of clinically significant mutations. However, the current manual trial recommendation process is time-intensive and lacks scalability.

This study introduces a computational framework to streamline and standardize clinical trial matching using natural language processing (NLP) and unsupervised clustering. Trial brief descriptions were analyzed to extract frequent terms, and trials were grouped based on term similarity using joint dimensionality reduction and clustering. …

Can Mirna Be The Missing Link Between Parkinson’S Disease And Pesticides?, Fatma Gobba

Theses and Dissertations

Parkinson’s disease (PD) is a common neurodegenerative condition that leads to significant morbidity and a decline in the quality of life. It develops as a consequence of the loss of dopaminergic neurons in the substantia nigra pars compacta. Nevertheless, the development of PD is influenced by environmental factors, and the intricate nature of these relationships is further complicated by a multitude of factors, including the genetic backgrounds that are specific to populations and variations in environmental exposures, such as pesticides. Pesticides, consisting of a diverse family of chemicals commonly used in both agricultural and household settings to protect crops against …

Sctiger: A Deep-Learning Method For Inferring Gene Regulatory Networks From Single-Cell Gene Expression Data, Madison Dautle

Theses and Dissertations

Inferring gene regulatory networks (GRNs) from single-cell RNA-sequencing (scRNA-seq) data is an important computational question to reveal fundamental regulatory mechanisms. Although many computational methods have been designed to predict GRNs, none work on condition specific GRNs by directly using paired datasets of case versus control experiments, common in diverse biological research projects. We present a novel deep-learning based method, scTIGER, for GRN detection by using the co-dynamics of gene expression. scTIGER also employs cell type-based pseudotiming, an attention-based convolutional neural network method, and permutation-based significance testing to infer GRNs from gene modules. We first applied scTIGER to scRNA-seq datasets of …

Vertical Federated Learning Using Autoencoders With Applications In Electrocardiograms, Wesley William Chorney

Theses and Dissertations

Federated learning is a framework in machine learning that allows for training a model while maintaining data privacy. Moreover, it allows clients with their own data to collaborate in order to build a stronger, shared model. Federated learning is of particular interest to healthcare data, since it is of the utmost importance to respect patient privacy while still building useful diagnostic tools. However, healthcare data can be complicated — data format might differ across providers, leading to unexpected inputs and incompatibility between different providers. For example, electrocardiograms might differ in sampling rate or number of leads used, meaning that a …