EmbPred30: Assessing 30-days Readmission for Diabetic Patients using Categorical Embeddings

TL;DR

This paper introduces a novel embedding-based deep neural network model that accurately predicts 30-day readmission of diabetic patients, potentially improving hospital care and reducing costs.

Contribution

It proposes a new embedding-based deep neural network model that outperforms existing models in predicting diabetic patient readmission within 30 days.

Findings

Achieved 95.2% accuracy in prediction

Achieved 97.4% AUROC in evaluation

Identified medication changes as a significant factor

Abstract

Hospital readmission is a crucial healthcare quality measure that helps in determining the level of quality of care that a hospital offers to a patient and has proven to be immensely expensive. It is estimated that more than $25 billion are spent yearly due to readmission of diabetic patients in the USA. This paper benchmarks existing models and proposes a new embedding based state-of-the-art deep neural network(DNN). The model can identify whether a hospitalized diabetic patient will be readmitted within 30 days or not with an accuracy of 95.2% and Area Under the Receiver Operating Characteristics(AUROC) of 97.4% on data collected from 130 US hospitals between 1999-2008. The results are encouraging with patients having changes in medication while admitted having a high chance of getting readmitted. Identifying prospective patients for readmission could help the hospital systems in improving their inpatient care, thereby saving them from unnecessary expenditures.