Biomedical Informatics Commons^™

A Specification-Driven Framework For Self-Supervised Learning In Specialized Vision Domains, Mahmut S. Gokmen

Theses and Dissertations--Computer Science

Self-supervised learning (SSL) has emerged as a principled approach to visual representation learning that derives supervisory signal directly from unlabeled data, enabling foundation models to be trained at scale without manual annotation. Deployments in medical imaging and biometric recognition have demonstrated the potential of this paradigm, yet the assumptions that make SSL effective on natural image benchmarks fail systematically in specialized domains. Generic SSL pipelines encode a tacit assumption that the most informative correspondence is spatial proximity within a single acquisition. In specialized domains this assumption breaks at the level of the data-generating process: the signal that carries domain-specific information …

Predicting Biologic Nonadherence In Inflammatory Bowel Disease Using Machine Learning Models, Christian Rhudy

Theses and Dissertations--Clinical and Translational Science

Inflammatory bowel disease is a chronic relapsing-remitting disease of the intestinal tract affecting approximately 0.5% of the population in the US. Moderate to severe inflammatory bowel disease is managed with biologic therapies to induce and maintain remission, however nonadherence to biologics occurs in and estimated 16.9% and 45% of patients. Nonadherence to biologics can increase the risk of disease flares, emergent healthcare utilization, and surgical intervention. While numerous factors have been associated with biologic nonadherence, there are limited resources for prospectively assessing patient-specific risk of biologic nonadherence. Accordingly, objective of this work is to develop machine learning methods of predicting …

Biomedical Relation Extraction In The Era Of (Large) Language Models, Yuhang Jiang

Theses and Dissertations--Computer Science

Biomedical relation extraction is a key task in information extraction supporting downstream applications such as knowledge discovery, information retrieval, and question answering. As such, improvements in relation extraction (RE) are expected to have major implications for other information needs in biomedicine. In this dis- sertation, we pursue biomedical relation extraction in several advanced settings with recent advances in large language models (LLMs) and retrieval augmented generation (RAG). First, we address an important slot filling task on social media that arose during the Covid-19 pandemic, using the idea of continuous prompts with encoder models. Next, we handle a tricky dynamic n-ary …

Enhancing Public Health Surveillance: Development And Validation Of Machine Learning Models For Suspected Opioid Overdose Detection In Emergency Medical Services Data, Peter J. Rock

Theses and Dissertations--Clinical and Translational Science

The ongoing opioid overdose crisis in the United States requires timely and accurate surveillance systems to inform public health responses. Traditional public health surveillance methods rely on hospital discharge data and death certificates, which suffer from significant reporting delays and miss cases where patients refuse hospital transportation. Emergency Medical Services (EMS) data presents a promising alternative with advantages in timeliness and case ascertainment but lacks validated definitions for suspected opioid overdose (SOO).

This dissertation addresses this critical gap through the development, validation, and fairness assessment of machine learning models with natural language processing (ML-NLP) for identifying SOOs in EMS data. …

Bioinformatic Analysis Of Proteomic And Genomic Data From Nsclc Tumors On Prognostic And Predictive Factors Of Immunotherapy Treatment, Mark Wuenschel

Theses and Dissertations--Pharmacy

Recent lung cancer research has led to advancements in molecular immunology, resulting in development of small molecule inhibitors, or immune checkpoint inhibitors, that propagate an anti-tumor T cell response. Despite increased overall and progression-free survival with reduced adverse effects compared to traditional chemotherapy, treating advanced stage lung adenocarcinoma patients remains non-curative, and evidence of non-responders or tumor recurrence to immune checkpoint inhibitor therapy is growing. Also, compared to traditional chemotherapy, there is a lower percentage of patients who respond to small molecule inhibitors. In this analysis of proteomic and genomic data from The Cancer Proteome Atlas and Global Data Commons …

Machine Learning Framework For Real-World Electronic Health Records Regarding Missingness, Interpretability, And Fairness, Jing Lucas Liu

Theses and Dissertations--Computer Science

Machine learning (ML) and deep learning (DL) techniques have shown promising results in healthcare applications using Electronic Health Records (EHRs) data. However, their adoption in real-world healthcare settings is hindered by three major challenges. Firstly, real-world EHR data typically contains numerous missing values. Secondly, traditional ML/DL models are typically considered black-boxes, whereas interpretability is required for real-world healthcare applications. Finally, differences in data distributions may lead to unfairness and performance disparities, particularly in subpopulations.

This dissertation proposes methods to address missing data, interpretability, and fairness issues. The first work proposes an ensemble prediction framework for EHR data with large missing …