Classification of Diagnosis-Related Groups using Computational Intelligence Techniques

Abstract

The optimization of the resources used in clinics and hospitals is a key problem in hospital management. In particular, how to improve the efficiency in procedures and treatments for patients, reducing cost, but without deteriorating the quality of the patient's stay is one of the greatest challenges faced by health providers. In this sense, the development of tools that can help health care providers to ensure that inpatients can be discharged at the times indicated by international standards according to their pathological condition is of great interest for the optimization of resources, especially in developing countries. There are different standards for grouping patients according to their diagnoses and procedures information, this work focuses on the Diagnosis-Related Groups (DRGs) patient classification system. Typically DRGs are obtained after patients' discharge, only for billing and payment purposes, which reduce the ability of health providers to take corrective actions when the health care attention deviates from the standard attention of specific patients' conditions.This work focuses in the use of Machine Learning (ML) techniques as an alternative to DRGs regular classification methods. The main aim is to evaluate whether ML methods are able to classify patients according to the DRGs standard, using the information available at the patient's discharge. This results would be the base line for further analysis focused on the prediction of DRGs in early stages of the patient's hospitalization. The results show that DRGs classification using Artificial Neural Networks and Ensemble methods can achieve up to 96% of accuracy in a real database of more than 82.910 health records.