2026-09-23T20:29:27Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/129587 2026-02-05 col_2142_5131 col_2142_8888 com_2142_5130 com_2142_8887 com_2142_234

application/pdf en text Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-05-01 The student, Ze Yang, accepted the attached license on 2025-04-26 at 14:46. The student, Ze Yang, submitted this Thesis for approval on 2025-04-26 at 15:01. This Thesis was approved for publication on 2025-04-28 at 12:38. DSpace SAF Submission Ingestion Package generated from Vireo submission #22008 on 2025-10-19 at 19:16:21 Identifying undiagnosed patients with rare genetic aortopathies using open-source large language models Yang, Ze 2025-04-28 Kindratenko, Volodymyr Large Language Model Medical Diagnosis eng Rare genetic aortopathies are frequently missed in clinical practice due to their phenotypic heterogeneity. Although timely genetic testing can prevent catastrophic cardiovascular events, current diagnostic pathways are based on primary care physicians to recognize subtle clinical indicators and initiate referrals. This dependency often leads to missed or delayed diagnoses, particularly in patients with atypical presentations. Broader and more systematic approaches are needed to identify at-risk individuals who fall outside conventional diagnostic patterns. Free-text clinical notes offer detailed, unstructured insights into a patient’s history that are often overlooked in automated systems. Given the ability of large language models (LLMs) to process unstructured text, we developed an open-source LLM-based pipeline that recommends genetic testing for rare aortopathies based on patient progress notes. The pipeline uses retrieval augmented generation (RAG) with a curated corpus of aortopathy-related knowledge to improve prediction accuracy, especially in ambiguous cases. We validated the pipeline using 22,510 notes from 500 individuals (250 diagnosed cases and 250 controls) in the Penn Medicine BioBank (PMBB). The model correctly identified 425 out of 499 patients, achieving a recom mendation accuracy of 0.852, precision of 0.889, recall of 0.803, F1 score of 0.844, and F3 score of 0.811. Our results show that LLMs can effectively analyze clinical notes to recommend genetic testing, enabling earlier detection of rare genetic aortopathies. The pipeline is generalizable, requires no pre-processing of notes, and can be adapted to other disease domains for broader clinical impact. 2025-05 Thesis https://hdl.handle.net/2142/129587 Copyright 2025 Ze Yang Electrical & Computer Eng Electrical & Computer Engr University of Illinois Urbana-Champaign M.S. Thesis