An exploration on methods for early prediction of sepsis

Chen, Zikun

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https://hdl.handle.net/2142/115743 Copy

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

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.

Graduation Semester

2022-05

Type of Resource

Thesis

Copyright and License Information

Copyright 2022 Zikun Chen

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