Hospital readmission is considered an effective measurement of care provided within healthcare. Being able to risk identify patients facing a high likelihood of unplanned hospital readmission in the next 30-days could allow for further investigation and possibly prevent the readmission. Current models, such as LACE, sacrifice accuracy in order to allow for end-users to have a straightforward and simple experience. This study acknowledges that while HbA1c is important, it may not be critical in predicting readmissions. It also investigates the hypothesis that using machine learning on a wide feature, making use of model diversity, and blending prediction will improve the accuracy of readmission risk predictions compared with existing techniques. A dataset originally containing 100,000 admissions and 56 features was used to evaluate the hypothesis. The results from the study are encouraging and can help healthcare providers improve inpatient diabetic care.
Published at ResearchGate