A decision support system for predicting the treatment of ectopic pregnancies. (September 2019)

• Record Type: Journal Article
• Authors: De Ramón Fernández, Alberto; Ruiz Fernández, Daniel; Prieto Sánchez, María Teresa
• Is Part Of: International journal of medical informatics. Volume 129(2019)
• Page Start: 198
• Page End: 204
• Publication Date: 2019-09
• Subjects: Aid decision algorithms -- Classifier -- Ectopics pregnancies -- Clinical treatment; 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.2019.06.002
• Languages: English
• ISSNs: 1386-5056

Abstract

Highlights: Misclassification in ectopic pregnancies can involve major complications, even death. Developed a three-stage classifier to predict the treatment for ectopic pregnancies. Testing with four different algorithms: MLP, SVM, deep learning, Naïves Bayes. Best results (obtained with SVM) presents a 96.1% of accuracy. Algorithms using SVM and MLP can be useful to help gynecologists in their decisions. Abstract: Background and objective: Ectopic pregnancy is an important cause of morbidity and mortality worldwide. An early diagnosis, as well as the choice of the most suitable treatment for the patient is crucial to avoid possible complications. According to different factors an ectopic pregnancy must be treated from surgery, using a pharmacological treatment or following a conservative treatment. In this paper, a clinical decision support systems based on artificial intelligence algorithms has been developed to help clinicians to choose the initial treatment to be followed by the patient. Methods: A decision support system based on a three stages classifier has been developed. Each stage acts as a filter and allows re-evaluation of the classification made in the previous stage in order to find diagnostic errors. This classifier has been implemented and tested for four different aid algorithms: Multilayer Perceptron, Deep Learning, Support Vector Machine and Naives Bayes. Results: The results prove that the evaluated algorithms Support Vector Machine and Multilayer Perceptron can be useful to help gynecologists in their decisions about initial treatment, especially with Support Vector Machine that presents accuracy, sensitivity and specificity outcomes about 96.1%, 96% and 98%, respectively. Conclusions: According to the results, it is feasible to develop a clinical decision support system using the algorithms that present a higher precision. This system would help gynecologists to take the most accurate decision about the initial treatment, thus avoiding future complications.