Effective knowledge extraction and knowledge-enhanced machine learning for health

Jiang, Pengcheng

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https://hdl.handle.net/2142/124296 Copy

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

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.

Graduation Semester

2024-05

Type of Resource

Thesis

Copyright and License Information

Copyright 2024 Pengcheng Jiang

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