A Healthcare management using clinical decision support system

Abstract

From the literature it is studied that, most of the medical error is due to faulty healthcare system. Due to this, there is treatment delay, that leads to complications in later stages of disease progression. Medical error caused due to the failure in healthcare system can be reduced by employing an appropriate clinical decision support system (CDSS). CDSS helps in identifying the severity of disease by predicting its progression. The treatment management of gallstone disease is considered as a case study in this paper.This paper presents a CDSS with the help of machine learning for improving the treatment management. CDSS with the help of a statistical comparator, identifies an efficient tool for finding the associated risk factors. These risk factors are then used to predict the disease progression and identify the cases that may need Endoscopic Retrograde Cholangio-Pancreatography (ERCP) as the treatment progresses. The model that learns and predicts accurately is selected, using the concept of Area Under Curve (AUC). For this purpose, a Modified Cascade Neural Network (ModCNN) built upon the architecture of Cascade-Correlation Neural Network (CCNN) is proposed and tested using an ADAptive LInear NEuron (ADALINE) circuit. It's performance is evaluated and compared with Artificial Neural Network (ANN) and CCNN.Using this prediction information, disease progression is analysed and proper treatment is initiated, thereby reducing the medical error. ModCNN showed better accuracy (96.42%) for predicting the disease progression when compared with CCNN (93.24%) and ANN (89.65%). Thus, CDSS presented here, assisted in reducing the medical error and providing better healthcare management. © 2018 IEEE.