{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/124296"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/124296","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Effective knowledge extraction and knowledge-enhanced machine learning for health","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms","abstract_has_math":false,"creators":["Jiang, Pengcheng"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Sun, Jimeng"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-05","date_published":"2024-05","updated_at":"2026-07-22T22:25:00Z","subjects":["Knowledge Graph","Machine Learning","Large Language Model","Healthcare Prediction","Molecule Property Prediction","Summarization","Prompting"],"languages":["en","eng"],"rights":["Copyright 2024 Pengcheng Jiang"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/124296","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Sun, Jimeng"]},{"key":"dc:creator","label":"Author","values":["Jiang, Pengcheng"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2024-05","2024-04-29"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Knowledge Graph","Machine Learning","Large Language Model","Healthcare Prediction","Molecule Property Prediction","Summarization","Prompting"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2024 Pengcheng Jiang"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/124296"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms","The student, Pengcheng Jiang, accepted the attached license on 2024-04-16 at 00:16.","The student, Pengcheng Jiang, submitted this Thesis for approval on 2024-04-16 at 00:32.","This Thesis was approved for publication on 2024-04-29 at 10:00.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20428 on 2024-09-16 at 00:34:37","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."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Effective knowledge extraction and knowledge-enhanced machine learning for health"]}]}],"canonical_facts":{"dc:contributor":["Sun, Jimeng"],"dc:creator":["Jiang, Pengcheng"],"dc:date":["2024-05","2024-04-29"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms","The student, Pengcheng Jiang, accepted the attached license on 2024-04-16 at 00:16.","The student, Pengcheng Jiang, submitted this Thesis for approval on 2024-04-16 at 00:32.","This Thesis was approved for publication on 2024-04-29 at 10:00.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20428 on 2024-09-16 at 00:34:37","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."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/124296"],"dc:language":["en","eng"],"dc:rights":["Copyright 2024 Pengcheng Jiang"],"dc:subject":["Knowledge Graph","Machine Learning","Large Language Model","Healthcare Prediction","Molecule Property Prediction","Summarization","Prompting"],"dc:title":["Effective knowledge extraction and knowledge-enhanced machine learning for health"],"dc:type":["text"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:00Z"}