Identifying Features that Impact Diabetes Mellitus Readmission Rates
Dr. Guenter Tusch; firstname.lastname@example.org
PURPOSE: Diabetes mellitus (DM) is a growing burden in the United States. Readmission of patients with diabetes has generated more concern in healthcare. The common predictors of readmission among the inpatient diabetes cohort include racial and socioeconomic factors, non-diabetes related co-morbidities, and failure to acknowledge diabetes at discharge. The goal of this paper is to identify the possible predictors for early and late readmissions of diabetes.
PROCEDURE: The dataset used for this paper was downloaded from the UCI Machine learning repository. There were several preprocessing steps performed on the data which reduced the features from 55 to 45 and the observations from 101,766 to 71,048 unique observations. The machine learning models used were Logistic regression and Random forest.
OUTCOME: The predictors identified for early readmission are admission type, discharge disposition, admission source, medical specialty, number of emergency visits, number of inpatient visits, primary, secondary and tertiary diagnosis, number of diagnosis, insulin intake, and diabetic medication. The predictors identified for late readmission are the same as early readmission with some additional predictors which are race, age, time in hospital, number of procedures, number of outpatient visits, A1c results and acarbose drug. There was no significant difference in the strength of this predictors comparing the common predictors for the early and late readmissions.
For the logistic regression model, the prediction accuracy for early and late readmission were 87% and 67% respectively. For Random forest model, the prediction accuracy for early and late readmission were 72% and 61% respectively. It was concluded that logistic regression model performed best in predicting both early and late readmission.
IMPACT: This project identifies the useful predictor of readmission rates which may prove valuable in the development of strategies to reduce readmission rates and costs for the care of individuals with diabetes mellitus.
Ajileye, Omotayo Emmanuel, "Identifying Features that Impact Diabetes Mellitus Readmission Rates" (2018). Technical Library. 314.