Award Date
8-1-2024
Degree Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Computer Science
First Committee Member
Kazem Taghva
Second Committee Member
Laxmi Gewali
Third Committee Member
Wolfgang Bein
Fourth Committee Member
Mingon Kang
Fifth Committee Member
Emma Regentova
Number of Pages
108
Abstract
In this work, we report on a series of natural language processing tools and models to improve the efficiency and accuracy of information discovery from clinical trials and pharmacological studies. Our main contributions are: 1. The development of an open-source platform Tri-AL that • Enables dynamic tracking of clinical trials information over time, • Excels in data visualization and user interaction with a particular emphasis on enhancing the analysis and representation of race and ethnicity data to foster equity in clinical research, and • Includes a predictive model utilizing machine learning to decipher drug mechanisms of action. 2. Heterogeneous Graph Neural Network for Gene-Chemical Entity Relation Extraction: We created a supervised deep learning model that adapts a heterogeneous Graph Neural Network to extract gene-chemical components. This model augments word representations using message passing that accurately identifies gene-chemical named entities and their relationships class. 3. Bipartite Graph Model for Evaluating Summarization Performance: We proposed a bipartite graph model to evaluate the performance of large language models in summarizing clinical trials. This model provides a robust framework to assess the accuracy and effectiveness of automated summarization tools in the medical domain.
Controlled Subject
Translating and interpreting; Patterns; Clinical trials; Medical records--Data processing
Disciplines
Engineering
File Format
File Size
2500KB
Degree Grantor
University of Nevada, Las Vegas
Language
English
Repository Citation
Esmail Zadeh Nojoo Kambar, Mina, "Harnessing NLP and Large Language Models for Pattern Discovery and Information Extraction in Electric Health Reports" (2024). UNLV Theses, Dissertations, Professional Papers, and Capstones. 5110.
https://digitalscholarship.unlv.edu/thesesdissertations/5110
Rights
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