Effective knowledge extraction and knowledge-enhanced machine learning for health
Jiang, Pengcheng
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https://hdl.handle.net/2142/124296
Description
Title
Effective knowledge extraction and knowledge-enhanced machine learning for health
Author(s)
Jiang, Pengcheng
Issue Date
2024-04-29
Director of Research (if dissertation) or Advisor (if thesis)
Sun, Jimeng
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Knowledge Graph
Machine Learning
Large Language Model
Healthcare Prediction
Molecule Property Prediction
Summarization
Prompting
Language
eng
Abstract
This work explores the frontier of knowledge extraction and its application in enhancing machine learning models, with a special focus on healthcare. Through innovative methodologies, it presents a novel approach to deriving structured knowledge from unstructured data, leveraging the power of pre-trained language models and sophisticated text analysis techniques. The work introduces groundbreaking strategies for optimizing knowledge graph completion tasks, evaluating the efficiency and accuracy of knowledge extraction from textual data, and revolutionizing text summarization to improve knowledge extraction processes. Furthermore, it delves into the application of this extracted knowledge in healthcare, demonstrating the potential of knowledge-enhanced machine learning in predicting healthcare outcomes and molecule properties with unprecedented precision. This research not only advances the field of knowledge extraction and machine learning but also opens up new avenues for future research and applications, particularly in enhancing the quality of healthcare and drug discovery. Through its innovative methodologies and significant findings, this thesis underscores the transformative potential of artificial intelligence in extracting and leveraging knowledge for scientific and medical advancements.
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