{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/115743"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/115743","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"An exploration on methods for early prediction of sepsis","abstract":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2024-05-01","abstract_html":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2024-05-01","abstract_has_math":false,"creators":["Chen, Zikun"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Sha, Lui R"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-05","date_published":"2022-05","updated_at":"2026-07-22T22:24:55Z","subjects":["sepsis prediction","health informatics"],"languages":["en","eng"],"rights":["Copyright 2022 Zikun Chen"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/115743","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Sha, Lui R"]},{"key":"dc:creator","label":"Author","values":["Chen, Zikun"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-05","2022-04-25"]},{"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":["sepsis prediction","health informatics"]}]},{"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 2022 Zikun Chen"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/115743"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2024-05-01","The student, Zikun Chen, accepted the attached license on 2022-04-21 at 17:36.","The student, Zikun Chen, submitted this Thesis for approval on 2022-04-21 at 17:45.","This Thesis was approved for publication on 2022-04-25 at 15:46.","DSpace SAF Submission Ingestion Package generated from Vireo submission #17899 on 2022-11-11 at 12:58:08","Sepsis is a potentially life-threatening condition that occurs when the body's response to an infection damages its own tissues \\cite{Mayo_sepsis_def}. Identification of Sepsis in its early stages is vital in preventing significant organ injury, prolonged hospitalization, and potentially death \\cite{mortality_per_hour}. The objective of this thesis is to build a pipeline for early sepsis prediction and examined each steps in the pipeline with the goal to explore different methods and algorithms that can be applied to mitigates the following problems with early sepsis prediction: 1. missingness of data, 2. mismeasurements within data, 3. complex structural relationship between features, 4. imbalance nature of data, and 5. the changing patient states and its corresponding distributions. This thesis had shed lights on the importance of the temporal aspect of medical data on the performance of predictive models in complex medical problems like early sepsis prediction. Further improvements of the prediction pipeline are needed and will be discussed in this thesis."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["An exploration on methods for early prediction of sepsis"]}]}],"canonical_facts":{"dc:contributor":["Sha, Lui R"],"dc:creator":["Chen, Zikun"],"dc:date":["2022-05","2022-04-25"],"dc:description":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2024-05-01","The student, Zikun Chen, accepted the attached license on 2022-04-21 at 17:36.","The student, Zikun Chen, submitted this Thesis for approval on 2022-04-21 at 17:45.","This Thesis was approved for publication on 2022-04-25 at 15:46.","DSpace SAF Submission Ingestion Package generated from Vireo submission #17899 on 2022-11-11 at 12:58:08","Sepsis is a potentially life-threatening condition that occurs when the body's response to an infection damages its own tissues \\cite{Mayo_sepsis_def}. Identification of Sepsis in its early stages is vital in preventing significant organ injury, prolonged hospitalization, and potentially death \\cite{mortality_per_hour}. The objective of this thesis is to build a pipeline for early sepsis prediction and examined each steps in the pipeline with the goal to explore different methods and algorithms that can be applied to mitigates the following problems with early sepsis prediction: 1. missingness of data, 2. mismeasurements within data, 3. complex structural relationship between features, 4. imbalance nature of data, and 5. the changing patient states and its corresponding distributions. This thesis had shed lights on the importance of the temporal aspect of medical data on the performance of predictive models in complex medical problems like early sepsis prediction. Further improvements of the prediction pipeline are needed and will be discussed in this thesis."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/115743"],"dc:language":["en","eng"],"dc:rights":["Copyright 2022 Zikun Chen"],"dc:subject":["sepsis prediction","health informatics"],"dc:title":["An exploration on methods for early prediction of sepsis"],"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":"2026-07-22T22:24:55Z"}