Khadijeh Paydar1, Sharareh R Niakan Kalhori2, Mahmoud Akbarian3, Abbas Sheikhtaheri4. 1. Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Islamic Republic of Iran. Electronic address: kh-paydar@alumnus.ac.ir. 2. Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Islamic Republic of Iran. Electronic address: sh-rniakank@sina.tums.ac.ir. 3. Rheumatology Research Center, Tehran University of Medical Sciences, Tehran, Islamic Republic of Iran. Electronic address: makbarian@tums.ac.ir. 4. Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Islamic Republic of Iran. Electronic address: sheikhtaheri.a@iums.ac.ir.
Abstract
OBJECTIVE: Pregnancy among systemic lupus erythematosus (SLE)-affected women is highly associated with poor obstetric outcomes. Predicting the risk of foetal outcome is essential for maximizing the success of pregnancy. This study aimed to develop a clinical decision support system (CDSS) to predict pregnancy outcomes among SLE-affected pregnant women. METHODS: We performed a retrospective analysis of 149 pregnant women with SLE, who were followed at Shariati Hospital (104 pregnancies) and a specialized clinic (45 pregnancies) from 1982 to 2014. We selected significant features (p<0.10) using a binary logistic regression model performed in IBM SPSS (version 20). Afterward, we trained several artificial neural networks (multi-layer perceptron [MLP] and radial basis function [RBF]) to predict the pregnancy outcome. In order to evaluate and select the most effective network, we used the confusion matrix and the receiver operating characteristic (ROC) curve. We finally developed a CDSS based on the most accurate network. MATLAB 2013b software was applied to design the neural networks and develop the CDSS. RESULTS: Initially, 45 potential variables were analysed by the binary logistic regression and 16 effective features were selected as the inputs of neural networks (P-value<0.1). The accuracy (90.9%), sensitivity (80.0%), and specificity (94.1%) of the test data for the MLP network were achieved. These measures for the RBF network were 71.4%, 53.3%, and 79.4%, respectively. Having applied a 10-fold cross-validation method, the accuracy for the networks showed 75.16% accuracy for RBF and 90.6% accuracy for MLP. Therefore, the MLP network was selected as the most accurate network for prediction of pregnancy outcome. CONCLUSION: The developed CDSS based on the MLP network can help physicians to predict pregnancy outcomes in women with SLE. Copyright Â
OBJECTIVE: Pregnancy among systemic lupus erythematosus (SLE)-affected women is highly associated with poor obstetric outcomes. Predicting the risk of foetal outcome is essential for maximizing the success of pregnancy. This study aimed to develop a clinical decision support system (CDSS) to predict pregnancy outcomes among SLE-affected pregnant women. METHODS: We performed a retrospective analysis of 149 pregnant women with SLE, who were followed at Shariati Hospital (104 pregnancies) and a specialized clinic (45 pregnancies) from 1982 to 2014. We selected significant features (p<0.10) using a binary logistic regression model performed in IBM SPSS (version 20). Afterward, we trained several artificial neural networks (multi-layer perceptron [MLP] and radial basis function [RBF]) to predict the pregnancy outcome. In order to evaluate and select the most effective network, we used the confusion matrix and the receiver operating characteristic (ROC) curve. We finally developed a CDSS based on the most accurate network. MATLAB 2013b software was applied to design the neural networks and develop the CDSS. RESULTS: Initially, 45 potential variables were analysed by the binary logistic regression and 16 effective features were selected as the inputs of neural networks (P-value<0.1). The accuracy (90.9%), sensitivity (80.0%), and specificity (94.1%) of the test data for the MLP network were achieved. These measures for the RBF network were 71.4%, 53.3%, and 79.4%, respectively. Having applied a 10-fold cross-validation method, the accuracy for the networks showed 75.16% accuracy for RBF and 90.6% accuracy for MLP. Therefore, the MLP network was selected as the most accurate network for prediction of pregnancy outcome. CONCLUSION: The developed CDSS based on the MLP network can help physicians to predict pregnancy outcomes in women with SLE. Copyright Â
Authors: Mário W L Moreira; Joel J P C Rodrigues; Neeraj Kumar; Jalal Al-Muhtadi; Valery Korotaev Journal: J Med Syst Date: 2018-02-01 Impact factor: 4.460