A clinical decision support system for prediction of pregnancy outcome in pregnant women with systemic lupus erythematosus

Paydar, Khadijeh; Kalhori, Sharareh R. Niakan; Akbarian, M.; Sheikhtaheri, Abbas

doi:10.1016/j.ijmedinf.2016.10.018

Cited by 66 publications

(39 citation statements)

References 53 publications

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“…Other example of the use of ANN in obstetrics is the work of Paydar et al [15], who experimented with two types of neural networks, MLP and radial basis function (RBF) for predicting pregnancy success in women with systemic lupus erythematous. From a sample of 149 women and the evaluation of 16 predictors, they were able to predict the success of pregnancy with an accuracy of 90.9%.…”

Section: Related Literaturementioning

confidence: 99%

A decision support system for predicting the treatment of ectopic pregnancies

Ramón-Fernández

Fernández

Prieto-Sánchez³

2019

International Journal of Medical Informatics

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This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Highlights • Misclassification in ectopic pregnancies can involve major complications, causing even death. • We have developed a three-stage classifier to predict the most suitable treatment for ectopic pregnancies. • Our classifier has been tested for four different algorithms: a multilayer perceptron, a support vector machine, a deep learning algorithm and Naïves Bayes. • The best results were obtained with Support Vector Machine algorithm that presents accuracy, sensitivity and specity outcomes about 96,1%, 96% and 98%, respectively.. • 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.

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Section: Related Literaturementioning

confidence: 99%

A decision support system for predicting the treatment of ectopic pregnancies

Ramón-Fernández

Fernández

Prieto-Sánchez³

2019

International Journal of Medical Informatics

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“…Pregnancy among SLE-affected women is highly associated with poor obstetric outcomes, namely fetal loss from spontaneous abortion or intrauterine death [58]. Paydar et al developed a CDSS to predict pregnancy outcomes among SLE-affected pregnant women, namely spontaneous abortion or live birth [37]. Two ANNs were trained based on features selected by a binary logistic regression (LR) model: a multi-layer perceptron (MLP) model and radial basis function (RBF) model.…”

Section: Chronic Disease Management and Pregnancymentioning

confidence: 99%

Enabling pregnant women and their physicians to make informed medication decisions using artificial intelligence

Davidson

Boland

2020

J Pharmacokinet Pharmacodyn

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The role of artificial intelligence (AI) in healthcare for pregnant women. To assess the role of AI in women's health, discover gaps, and discuss the future of AI in maternal health. A systematic review of English articles using EMBASE, PubMed, and SCOPUS. Search terms included pregnancy and AI. Research articles and book chapters were included, while conference papers, editorials and notes were excluded from the review. Included papers focused on pregnancy and AI methods, and pertained to pharmacologic interventions. We identified 376 distinct studies from our queries. A final set of 31 papers were included for the review. Included papers represented a variety of pregnancy concerns and multidisciplinary applications of AI. Few studies relate to pregnancy, AI, and pharmacologics and therefore, we review carefully those studies. External validation of models and techniques described in the studies is limited, impeding on generalizability of the studies. Our review describes how AI has been applied to address maternal health, throughout the pregnancy process: preconception, prenatal, perinatal, and postnatal health concerns. However, there is a lack of research applying AI methods to understand how pharmacologic treatments affect pregnancy. We identify three areas where AI methods could be used to improve our understanding of pharmacological effects of pregnancy, including: (a) obtaining sound and reliable data from clinical records (15 studies), (b) designing optimized animal experiments to validate specific hypotheses (1 study) to (c) implementing decision support systems that inform decision-making (11 studies). The largest literature gap that we identified is with regards to using AI methods to optimize translational studies between animals and humans for pregnancyrelated drug exposures.

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“…In 2017, a group of researchers were able to predict pregnancy outcomes among Systemic lupus erythematosus affected women with the help of a Clinical Decision Support System (CDSS), a condition which is highly associated with poor obstetric outcomes. Based on the values for 16 factors the CDSS predicts the outcome of the pregnancy as either spontaneous abortion or live birth [15]. [6] found that CDSSs improved practitioner performance in 64% of the studies, the result includes that 4 of 10 diagnostic systems, 16 of 21 reminder systems, 23 of 37 disease control systems, and 19 of 29 drug dosing or prescribing systems.…”

Section: Related Workmentioning

confidence: 99%

Enriching Quality of Maternal Health Care through Machine

Bolanle¹,

Olajide²

2020

IJCA

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Pregnancy outcomes rank the most pressing reproductive health problems in the world globally. Most maternal complications and deaths occur as a result of insufficient quality of care during pregnancy and labour. In most parts of the developing world, access to quality health care is limited and people depend on the health care providers who have limited training. Advancements in medical technology have drastically increased the quantity of data available in the healthcare industry, ranging from patient reports and genomic data to electronic medical records. These data can provide a wide scope of insights into patient cases for prevention and cure on health issues. Thus, this research aimed at designing a machine learning-based decision support system for maternal health care. The system is designed using Unified Modeling Language (UML) tools and a multi-class Support Vector Machine (SVM) was developed for the Decision Support System for Maternity Health Care (DSSMC). A Web-based DSSMC was developed and tested to facilitate automatic diagnosis of patient and to solve the problem of human error and bias.

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A clinical decision support system for prediction of pregnancy outcome in pregnant women with systemic lupus erythematosus

Cited by 66 publications

References 53 publications

A decision support system for predicting the treatment of ectopic pregnancies

A decision support system for predicting the treatment of ectopic pregnancies

Enabling pregnant women and their physicians to make informed medication decisions using artificial intelligence

Enriching Quality of Maternal Health Care through Machine

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