Download Decision support system design for preventing icu readmission

A DECISION SUPPORT SYSTEM DESIGN FOR PREVENTING ICU
READMISSION
Yu Shu
Advisor: Dr. Susan Lu
Committee Members: Dr. Sang Won Yoon and Dr. Yong Wang
Friday, May 5th, 2017, 3:30 pm to 5:30 pm. EB-R3
ABSTRACT
In this study, a new decision support system (DSS) is proposed for identifying patients at high risk
of readmissions to intensive care units (ICU) within 24 to 72 hours after discharge. ICU
readmission prediction is a complex problem, in which physiological variable selection,
interactions of variables and imbalance data are involved. Previously proposed approaches, such
as a scoring system (APACHEII) or classical data mining techniques (e.g. logistic regression,
decision trees, etc.) did not tackle these issues. We herein aim to develop a new integrated
framework, in which a prediction model is based on highly important physiological variables, such
as blood pressure, heart rate, respiratory rate, temperature, arterial base excess, arterial pH,
Hematocrit, platelets, red blood cell count, Albumin, Calcium, Creatinine, Magnesium, Potassium,
Sodium, and white blood cell count. The combination of under-sampling method and Synthetic
minority over-sampling technique (SMOTE) is applied to handle imbalanced data. Sequential
forward selection (SFS) is proposed to select the best predictors of ICU readmission prediction.
Principal component analysis (PCA) is applied for feature extraction. Backpropagation neural
network (BPNN) is proposed to predict ICU readmission. Multi-parameter Intelligent Monitoring in
Intensive Care (MIMIC II) research database is used for the validation of our proposed approach.
The experimental results show a comparable prediction performance (accuracy, sensitivity, and
specificity > 70%). Furthermore, an interactive tool is designed for real-time decision making with
visualizing prediction summary, including risk level and key variable control ranges.