Computer Sciences Commons^™

Flexible Attenuation Fields: Tomographic Reconstruction From Heterogeneous Datasets, Clifford S. Parker

Theses and Dissertations--Computer Science

Traditional reconstruction methods for X-ray computed tomography (CT) are highly constrained in the variety of input datasets they admit. Many of the imaging settings -- the incident energy, field-of-view, effective resolution -- remain fixed across projection images, and the only real variance is in the detector's position and orientation with respect to the scene. In contrast, methods for 3D reconstruction of natural scenes are extremely flexible to the geometric and photometric properties of the input datasets, readily accepting and benefiting from images captured under varying lighting conditions, with different cameras, and at disparate points in time and space. Extending CT …

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 …

Deep Neural Architectures For End-To-End Relation Extraction, Tung Tran

Theses and Dissertations--Computer Science

The rapid pace of scientific and technological advancements has led to a meteoric growth in knowledge, as evidenced by a sharp increase in the number of scholarly publications in recent years. PubMed, for example, archives more than 30 million biomedical articles across various domains and covers a wide range of topics including medicine, pharmacy, biology, and healthcare. Social media and digital journalism have similarly experienced their own accelerated growth in the age of big data. Hence, there is a compelling need for ways to organize and distill the vast, fragmented body of information (often unstructured in the form of natural …

Relation Prediction Over Biomedical Knowledge Bases For Drug Repositioning, Mehmet Bakal

Theses and Dissertations--Computer Science

Identifying new potential treatment options for medical conditions that cause human disease burden is a central task of biomedical research. Since all candidate drugs cannot be tested with animal and clinical trials, in vitro approaches are first attempted to identify promising candidates. Likewise, identifying other essential relations (e.g., causation, prevention) between biomedical entities is also critical to understand biomedical processes. Hence, it is crucial to develop automated relation prediction systems that can yield plausible biomedical relations to expedite the discovery process. In this dissertation, we demonstrate three approaches to predict treatment relations between biomedical entities for the drug repositioning task …

Rule Mining And Sequential Pattern Based Predictive Modeling With Emr Data, Orhan Abar

Theses and Dissertations--Computer Science

Electronic medical record (EMR) data is collected on a daily basis at hospitals and other healthcare facilities to track patients’ health situations including conditions, treatments (medications, procedures), diagnostics (labs) and associated healthcare operations. Besides being useful for individual patient care and hospital operations (e.g., billing, triaging), EMRs can also be exploited for secondary data analyses to glean discriminative patterns that hold across patient cohorts for different phenotypes. These patterns in turn can yield high level insights into disease progression with interventional potential. In this dissertation, using a large scale realistic EMR dataset of over one million patients visiting University of …

Deep Neural Networks For Multi-Label Text Classification: Application To Coding Electronic Medical Records, Anthony Rios

Theses and Dissertations--Computer Science

Coding Electronic Medical Records (EMRs) with diagnosis and procedure codes is an essential task for billing, secondary data analyses, and monitoring health trends. Both speed and accuracy of coding are critical. While coding errors could lead to more patient-side financial burden and misinterpretation of a patient’s well-being, timely coding is also needed to avoid backlogs and additional costs for the healthcare facility. Therefore, it is necessary to develop automated diagnosis and procedure code recommendation methods that can be used by professional medical coders.

The main difficulty with developing automated EMR coding methods is the nature of the label space. The …

Scalable Feature Selection And Extraction With Applications In Kinase Polypharmacology, Derek Jones

Theses and Dissertations--Computer Science

In order to reduce the time associated with and the costs of drug discovery, machine learning is being used to automate much of the work in this process. However the size and complex nature of molecular data makes the application of machine learning especially challenging. Much work must go into the process of engineering features that are then used to train machine learning models, costing considerable amounts of time and requiring the knowledge of domain experts to be most effective. The purpose of this work is to demonstrate data driven approaches to perform the feature selection and extraction steps in …

Context-Aware Debugging For Concurrent Programs, Justin Chu

Theses and Dissertations--Computer Science

Concurrency faults are difficult to reproduce and localize because they usually occur under specific inputs and thread interleavings. Most existing fault localization techniques focus on sequential programs but fail to identify faulty memory access patterns across threads, which are usually the root causes of concurrency faults. Moreover, existing techniques for sequential programs cannot be adapted to identify faulty paths in concurrent programs. While concurrency fault localization techniques have been proposed to analyze passing and failing executions obtained from running a set of test cases to identify faulty access patterns, they primarily focus on using statistical analysis. We present a novel …