2026-09-24T07:42:17Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/113924 2023-07-11 col_2142_5131 col_2142_8888 com_2142_5130 com_2142_8887 com_2142_234

2021-12 Iyer, Ravishankar K. 2022-04-29T21:35:52Z Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-04-06 without embargo terms The student, Haotian Chen, accepted the attached license on 2021-12-08 at 14:24. The student, Haotian Chen, submitted this Thesis for approval on 2021-12-08 at 14:37. This Thesis was approved for publication on 2021-12-08 at 15:10. DSpace SAF Submission Ingestion Package generated from Vireo submission #17416 on 2022-04-06 at 17:11:11 Made available in DSpace on 2022-04-29T21:35:52Z (GMT). No. of bitstreams: 2 CHEN-THESIS-2021.pdf: 1483347 bytes, checksum: afec77f6a1407d38fc123d8795b5978c (MD5) LICENSE.txt: 4209 bytes, checksum: 2f35e6eecda84193b4b716da254c6b7e (MD5) Previous issue date: 2021-12-08 application/pdf en text Multi-stage prognosis of COVID-19 using a clinical event-based stratification of disease severity Chen, Haotian 2021-12-08 Engineering 2022-04-29T21:35:52Z The COVID-19 disease has shown remarkable diversity in its manifestation. Precise anticipation of these manifestations is important to enable earlier intervention for high-risk patients and efficient deployment of medical resources. In this thesis, a multi-stage prognostic framework is developed for assessing COVID-19 patients at hospital admission and during disease progression. The analysis is conducted upon 10,123 COVID-19 patients treated at Rush University Medical Center at Chicago between 03/17/2020 and 08/07/2020. In order to characterize the patients with different severity, a stratification scheme is first established to assign patients to different stages of disease severity based on discrete clinical events (i.e., admission to hospital, admission to ICU, mechanical ventilation, and death). Then two prognostic frameworks were developed to predict the progression of COVID-19 through these stages: 1) a baseline model which uses the measurements collected at hospital admission to predict disease escalation to severe stages; 2) a progressive model which uses the measurements collected at the patient’s latest stage to predict further escalation. It is found that future clinical stages can be predicted using baseline measurements with clinically significant accuracy. Finally, key risk factors are identified using Least Absolute Shrinkage and Selection Operator (LASSO) and decision tree algorithms. The developed multi-stage framework can be used to anticipate COVID-19 disease progression, allowing earlier interventions as well as better management of hospital resources. Thesis eng http://hdl.handle.net/2142/113924 Copyright 2021 Haotian Chen Electrical & Computer Eng Electrical & Computer Engr University of Illinois at Urbana-Champaign Thesis M.S.