CCE Theses and Dissertations
Date of Award
2018
Document Type
Dissertation
Degree Name
Doctor of Philosophy in Computer Science (CISD)
Department
College of Engineering and Computing
Advisor
Sumitra Mukherjee
Committee Member
Michael Laszlo
Committee Member
Francisco Mitropoulos
Keywords
Sepsis, Septic Shock, Machine Learning, Prediction, Predictive Model, Classification, Ensemble Classifier
Abstract
Sepsis is an organ dysfunction life-threatening disease that is caused by a dysregulated body response to infection. Sepsis is difficult to detect at an early stage, and when not detected early, is difficult to treat and results in high mortality rates. Developing improved methods for identifying patients in high risk of suffering septic shock has been the focus of much research in recent years. Building on this body of literature, this dissertation develops an improved method for septic shock prediction. Using the data from the MMIC-III database, an ensemble classifier is trained to identify high-risk patients. A robust prediction model is built by obtaining a risk score from fitting the Cox Hazard model on multiple input features. The score is added to the list of features and the Random Forest ensemble classifier is trained to produce the model. The Cox Enhanced Random Forest (CERF) proposed method is evaluated by comparing its predictive accuracy to those of extant methods.
NSUWorks Citation
Aiman A. Darwiche. 2018. Machine Learning Methods for Septic Shock Prediction. Doctoral dissertation. Nova Southeastern University. Retrieved from NSUWorks, College of Engineering and Computing. (1051)
https://nsuworks.nova.edu/gscis_etd/1051.