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31. Predicting 30-Day ICU Readmission Rates Using Machine Learning on Electronic Health Records

open_access
Open Access
Author:
Farooq, Sarah
Title:
Predicting 30-Day ICU Readmission Rates Using Machine Learning on Electronic Health Records
Area of Honors:
Data Sciences
Keywords:
machine learning
classification
EHR
data
electronic health records
hospital
ICU
readmission
class imbalance
missing values
imputation
rebalancing
hospital readmission
patient
medical data
XG Boost
MICE data imputation
health
health care
model
supervised learning
model performance
File:
Download SarahFarooq_Honors_Thesis.pdf
Thesis Supervisors:
Fenglong Ma, Thesis Supervisor
John Yen, Thesis Honors Advisor