A decision support system for predicting the treatment of ectopic pregnancies

Please use this identifier to cite or link to this item: http://hdl.handle.net/10045/93788
Información del item - Informació de l'item - Item information
Title: A decision support system for predicting the treatment of ectopic pregnancies
Authors: Ramón-Fernández, Alberto de | Ruiz-Fernandez, Daniel | Prieto Sánchez, María Teresa
Research Group/s: Ingeniería Bioinspirada e Informática para la Salud
Center, Department or Service: Universidad de Alicante. Departamento de Tecnología Informática y Computación
Keywords: Aid decision algorithms | Classifier | Ectopics pregnancies | Clinical treatment
Knowledge Area: Arquitectura y Tecnología de Computadores
Issue Date: Sep-2019
Publisher: Elsevier
Citation: International Journal of Medical Informatics. 2019, 129: 198-204. doi:10.1016/j.ijmedinf.2019.06.002
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.
Sponsor: This work has been granted by the Ministerio de Economá y Competitividad of the Spanish Government (ref. TIN2014-53067-C3-1-R) and Alberto De Ramón Fernández is supported by grant BES-2015-073611.
URI: http://hdl.handle.net/10045/93788
ISSN: 1386-5056 (Print) | 1872-8243 (Online)
DOI: 10.1016/j.ijmedinf.2019.06.002
Language: eng
Type: info:eu-repo/semantics/article
Rights: © 2019 Elsevier B.V.
Peer Review: si
Publisher version: https://doi.org/10.1016/j.ijmedinf.2019.06.002
Appears in Collections:INV - IBIS - Artículos de Revistas

Files in This Item:
Files in This Item:
File Description SizeFormat 
Thumbnail2019_De-Ramon_etal_IntJMedicalInformatics_final.pdfVersión final (acceso restringido)714,19 kBAdobe PDFOpen    Request a copy
Thumbnail2019_De-Ramon_etal_IntJMedicalInformatics_accepted.pdfEmbargo 12 meses (acceso abierto: 10 jun. 2020)578,4 kBAdobe PDFOpen    Request a copy

Items in RUA are protected by copyright, with all rights reserved, unless otherwise indicated.