An exploration on methods for early prediction of sepsis
Chen, Zikun
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https://hdl.handle.net/2142/115743
Description
Title
An exploration on methods for early prediction of sepsis
Author(s)
Chen, Zikun
Issue Date
2022-04-25
Director of Research (if dissertation) or Advisor (if thesis)
Sha, Lui R
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
sepsis prediction
health informatics
Language
eng
Abstract
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.
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