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Xiaobei Zhou: Prediction Models for Serious Outcome and Death in Patients with Non-specific Complaints Presenting to the Emergency Department
Adviser: Prof. Dr. Werner Stahel
Co-Adviser: Dr. Markus Kalisch
August 2011
Abstract:
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This paper is based on the Basel
Non-specific Complaints (BANC) by Nemec, Koller,
Nickel, Maile, Winterhalder, Karrer, Laifer, and Bingisser [2010].
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Nonspecific complaints
(NSCs) are very common in emergency departments (EDs). However, when
treating the patients with NSCs, emergency physicians have rarely experience.
My research mainly focuses on the outcome variables a serious condition (o ser) and death in ED patients with NSCs. My
primary goal is to find a set of methods (classifiers) which classify with high
accuracies for o ser and death. Moreover, we try to find a series of risk factors (explanatory
variables) which are highly correlated with the outcome variables.
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We do not find a classifier that clearly outperforms all others in
all aspects. Random-Forest, Logistic-Regression and Adaboost turn out to be
favorable according to different criteria. We find that dealing with missing
values using imputation increases classification performance. Finally, we
discuss SMOTE as an interesting but not fully satisfy method for dealing with
highly unbalanced data.
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Download pdf (512 KB)
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