Publication Date
2025
Document Type
Thesis
Committee Members
Tanvi Banerjee, Ph.D. (Advisor); Timothy T. Phamduy, D.O. (Committee Member); Tomojit Ghosh, Ph.D. (Committee Member)
Degree Name
Master of Science (MS)
Abstract
Sepsis is a leading cause of pediatric mortality, claiming more lives in the United States annually than all childhood cancers combined. Early identification in Emergency Departments (EDs) remains challenging, as the current Phoenix criteria establishes an updated international consensus definition for sepsis, however is not designed for use as a screening tool. This study aimed to develop predictive models identifying pediatric patients at risk of sepsis within 24 hours of admission. Multiple tree-based and deep learning models were trained utilizing clinical and laboratory data from the initial four hours of presentation. Both the LightGBM and LSTM architectures demonstrated superior performance, with the LightGBM model achieving an AUC of 0.93 and a recall of 0.96. Predictions were primarily driven by dynamic changes in mean arterial pressure, respiratory rate, and heart rate. Furthermore, unsupervised K-means clustering identified two clinically distinct subgroups within the sepsis cohort, exhibiting phenotypes consistent with early Sepsis-Induced Coagulopathy (SIC) and critical Overt Disseminated Intravascular Coagulation (DIC). These findings highlight the potential of advanced machine learning for both early prediction and physiological stratification in pediatric sepsis management.
Page Count
84
Department or Program
Department of Computer Science and Engineering
Year Degree Awarded
2025
Copyright
Copyright 2025, all rights reserved. My ETD will be available under the "Fair Use" terms of copyright law.
