A clinical decision support system for prediction of pregnancy outcome in pregnant women with systemic lupus erythematosus. (January 2017)

• Record Type: Journal Article
• Authors: Paydar, Khadijeh; Niakan Kalhori, Sharareh R.; Akbarian, Mahmoud; Sheikhtaheri, Abbas
• Is Part Of: International journal of medical informatics. Volume 97(2017)
• Page Start: 239
• Page End: 246
• Publication Date: 2017-01
• Subjects: Artificial neural network -- Clinical decision support system -- Pregnancy outcomes -- Pregnancy complications -- Premature birth -- Stillbirth -- Systemic lupus erythematosus; Medical informatics -- Periodicals; Information science -- Periodicals; Computers -- Periodicals; Medical technology -- Periodicals; Medical Informatics -- Periodicals; Technology, Medical -- Periodicals; Computers; Information science; Medical informatics; Medical technology; Electronic journals; Periodicals; Electronic journals; 610.285
• DOI: 10.1016/j.ijmedinf.2016.10.018
• Languages: English
• ISSNs: 1386-5056

Abstract

Graphical abstract: Highlights: There are many complexities for predicting pregnancy outcomes in women with systemic lupus erythematosus (SLE). There is no clinical decision support system to classify pregnant women into two groups regarding their pregnancy outcome. We developed a CDSS for predicting pregnancy outcomes in SLE-affected women based on the different features. This CDSS developed based on a neural network model can be applied to predict pregnancy outcomes in SLE-affected women. 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.