30-day Hospital Readmission Prediction using MIMIC Data

Abstract

Patient readmission to the hospital within 30 days or 365 days is a challenging problem for hospitals as they get penalized and in many cases the Center of Medicaid and Medicare (CMS) will not reimburse the hospitals for the costs associated with these readmissions. Although readmission prediction is a common problem in healthcare and has been addressed by the researchers in the machine learning community, it remains a hard problem to solve. The goal of the project proposed in this paper is to build a predictive model for 30-day readmission based on the Medical Information Mart for Intensive Care (MIMIC III) dataset, which contains admissions for intensive care unit (ICU) patients. We used ICD9 embedding’s, chart events and demographics as features to train multiple classifiers including Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR) and Multi-Layer Perceptron (MLP). Best model, Random Forest, achieved 0.65 accuracy and 0.66 Area Under the Curve (AUC).