Immature ICU discharge leads to early readmission. Most of the ICU early readmissions can be avoided and a significant amount of treatment costs can be saved. To be able to successfully predict readmission, prediction model should be interpretable so that the issues ignored while discharging a patient from ICU are properly identified. Complex machine learning models are not easily interpretable and consequently they are ineffective for clinicians. Therefore, our aim was to develop a prediction model which achieves a high discriminating characteristic and also is interpretable enough so it can be used to prevent the occurrence of early readmissions. In this project, we separated feature selection and modeling steps to build a robust predictive pipeline to predict 30-day all-cause ICU readmissions. Our model identified 24 highly predictive features which at best achieved prediction with 90% accuracy using simple logistic regression.
The entire project report can be accessed at the link below:
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