Effective knowledge extraction and knowledge-enhanced machine learning for health

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.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2024

Author dc:creator

• Jiang, Pengcheng

Contributors dc:contributor

• Sun, Jimeng

• Knowledge Graph
• Machine Learning
• Large Language Model
• Healthcare Prediction
• Molecule Property Prediction
• Summarization
• Prompting

Statement dc:rights

• Copyright 2024 Pengcheng Jiang

Language dc:language

en, eng

Handle dc:identifier

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

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F124296