{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/125712"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/125712","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Towards robust clinical predictive modeling with heterogeneous electronic health record data","abstract":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-08-01","abstract_html":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-08-01","abstract_has_math":false,"creators":["Wu, Zhenbang"],"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":"2024-07-15","date_published":"2024-07-15","updated_at":"2026-07-22T22:25:02Z","subjects":["Deep Learning For Healthcare","Clinical Predictive Modeling","Electronic Health Records"],"languages":["en","eng"],"rights":["Copyright 2024 Zhenbang Wu"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/125712","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":["Wu, Zhenbang"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2024-07-15","2024-08"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"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":["Deep Learning For Healthcare","Clinical Predictive Modeling","Electronic Health Records"]}]},{"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 Zhenbang Wu"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/125712"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-08-01","The student, Zhenbang Wu, accepted the attached license on 2024-07-09 at 19:28.","The student, Zhenbang Wu, submitted this Thesis for approval on 2024-07-09 at 19:37.","This Thesis was approved for publication on 2024-07-15 at 11:43.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21028 on 2025-02-04 at 21:16:47","With the widespread adoption of Electronic Health Record (EHR) systems, there has been increasing interest in leveraging deep learning for clinical predictive modeling. However, existing models typically assume a uniform feature and label space. In contrast, different hospitals often use varied EHR systems with unique schemas (i.e., feature space). Additionally, the clinical tasks (i.e., label space) can change dynamically. In this work, we present two methods to address discrepancies in feature and label spaces across different healthcare settings. AutoMap is designed to enable the deployment of clinical predictive models across hospitals with diverse medical coding systems. It automatically aligns medical codes across different EHR systems via ontology-level alignment and code-level refinement. EDGE is designed to recommend newly developed drugs, which often lack extensive historical prescription data. By formulating new drug recommendation as a few-shot learning problem, it employs a drug-dependent multi-phenotype few-shot learner to quickly adapt to new drugs. We validate both methods using real-world EHR datasets from MIMIC-III, MIMIC-IV, eICU, and Claims databases. Our results demonstrate their effectiveness in addressing the challenges posed by unmatched feature and label spaces in clinical predictive modeling."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Towards robust clinical predictive modeling with heterogeneous electronic health record data"]}]}],"canonical_facts":{"dc:contributor":["Sun, Jimeng"],"dc:creator":["Wu, Zhenbang"],"dc:date":["2024-07-15","2024-08"],"dc:description":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-08-01","The student, Zhenbang Wu, accepted the attached license on 2024-07-09 at 19:28.","The student, Zhenbang Wu, submitted this Thesis for approval on 2024-07-09 at 19:37.","This Thesis was approved for publication on 2024-07-15 at 11:43.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21028 on 2025-02-04 at 21:16:47","With the widespread adoption of Electronic Health Record (EHR) systems, there has been increasing interest in leveraging deep learning for clinical predictive modeling. However, existing models typically assume a uniform feature and label space. In contrast, different hospitals often use varied EHR systems with unique schemas (i.e., feature space). Additionally, the clinical tasks (i.e., label space) can change dynamically. In this work, we present two methods to address discrepancies in feature and label spaces across different healthcare settings. AutoMap is designed to enable the deployment of clinical predictive models across hospitals with diverse medical coding systems. It automatically aligns medical codes across different EHR systems via ontology-level alignment and code-level refinement. EDGE is designed to recommend newly developed drugs, which often lack extensive historical prescription data. By formulating new drug recommendation as a few-shot learning problem, it employs a drug-dependent multi-phenotype few-shot learner to quickly adapt to new drugs. We validate both methods using real-world EHR datasets from MIMIC-III, MIMIC-IV, eICU, and Claims databases. Our results demonstrate their effectiveness in addressing the challenges posed by unmatched feature and label spaces in clinical predictive modeling."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/125712"],"dc:language":["en","eng"],"dc:rights":["Copyright 2024 Zhenbang Wu"],"dc:subject":["Deep Learning For Healthcare","Clinical Predictive Modeling","Electronic Health Records"],"dc:title":["Towards robust clinical predictive modeling with heterogeneous electronic health record data"],"dc:type":["text","Thesis"],"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":"2026-07-22T22:25:02Z"}