Towards a Gamete Matching Platform: Using Immunogenetics and Artificial Intelligence to Predict Recurrent Miscarriage

Mora-Sánchez, Aldo; Aguilar-Salvador, Daniel-Isui; Nowak, Izabela

doi:10.1101/534594

Cited by 2 publications

(4 citation statements)

References 28 publications

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“…Mora-Sánchez et al [4] proposed an ML-based prediction model showing the relationship between recurrent miscarriage and the human leukocyte antigen (HLA) genes according to predicted values. Using a support vector machine (SVM) with a linear kernel as a classifier, the study detected the risk of recurrent miscarriage with an accuracy of 67% and an AUC of 71%.…”

Section: Related Workmentioning

confidence: 99%

“…In addition, women can experience health conditions that occur only during pregnancy, such as gestational diabetes, preeclampsia, and eclampsia [2,3]. Numerous studies have investigated pregnancy loss using artificial intelligence (AI) and ML to predict the risk factors of miscarriage by collecting data from pregnant women or women who had experienced pregnancy loss [4]. This study developed ML prediction models to help obstetricians make accurate and timely decisions to avoid the risk of miscarriage.…”

Section: Introductionmentioning

confidence: 99%

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An Automated System for Early Prediction of Miscarriage in the First Trimester Using Machine Learning

Aljameel¹,

Aljabri²,

Aslam³

et al. 2023

Computers, Materials &Amp; Continua

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Currently, the risk factors of pregnancy loss are increasing and are considered a major challenge because they vary between cases. The early prediction of miscarriage can help pregnant ladies to take the needed care and avoid any danger. Therefore, an intelligent automated solution must be developed to predict the risk factors for pregnancy loss at an early stage to assist with accurate and effective diagnosis. Machine learning (ML)-based decision support systems are increasingly used in the healthcare sector and have achieved notable performance and objectiveness in disease prediction and prognosis. Thus, we developed a model to help obstetricians predict the probability of miscarriage using ML. And support their decisions and expectations about pregnancy status by providing an easy, automated way to predict miscarriage at early stages using ML tools and techniques. Although many published papers proposed similar models, none of them used Saudi clinical data. Our proposed solution used ML classification algorithms to build a miscarriage prediction model. Four classifiers were used in this study: decision tree (DT), random forest (RF), k-nearest neighbor (KNN), and gradient boosting (GB). Accuracy, Precision, Recall, F1-score, and receiver operating characteristic area under the curve (ROC-AUC) were used to evaluate the proposed model. The results showed that GB overperformed the other classifiers with an accuracy of 93.4% and ROC-AUC of 97%. This proposed model can assist in the early identification of at-risk pregnant women to avoid miscarriage in the first trimester and will improve the healthcare sector in Saudi Arabia.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Automated System for Early Prediction of Miscarriage in the First Trimester Using Machine Learning

Aljameel¹,

Aljabri²,

Aslam³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…Research in ART is represented well due to the fact that pharmacological intervention is necessary for several common ART practices. AI methods have been applied to inform and advise physicians [14], to predict pregnancy success [15][16][17][18], to provide optimized treatment [19], and to understand miscarriage risk [20].…”

Section: Assisted Reproductive Technologiesmentioning

confidence: 99%

“…Mora-Sánchez et al propose a methodology to analyze Human Leukocyte Antigen haplotypes from couples with recurrent miscarriage and couples with histories of successful pregnancies [20]. The SVM classifier with a linear kernel was used to predict the recurrent miscarriage and healthy pregnancy classes.…”

Section: Assisted Reproductive Technologiesmentioning

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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Towards a Gamete Matching Platform: Using Immunogenetics and Artificial Intelligence to Predict Recurrent Miscarriage

Cited by 2 publications

References 28 publications

An Automated System for Early Prediction of Miscarriage in the First Trimester Using Machine Learning

An Automated System for Early Prediction of Miscarriage in the First Trimester Using Machine Learning

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

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