Experience in machine learning application to predict pregnancy loss after assisted reproductive technologies

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Relevance: Machine learning (ML) method of data analysis makes it possible to thoroughly analyze the predictors of pregnancy loss after assisted reproductive technologies (ART). Prediction of live birth rate in ART program can be made using traditional mathematical models. However, ML enables to discover hidden patterns in nonlinear relationships and determine additional correctable factors.

Objective: Prediction of miscarriage in patients who undergo infertility treatment using ART methods based on clinical, anamnestic and embryological parameters, using the decision tree algorithm combined with linear regression.

Materials and methods: The retrospective study included 1021 married couples. The study analyzed the results of clinical and laboratory examination and the parameters of stimulated cycle depending on the rates of pregnancy and miscarriage after ART using linear regression and decision tree.

Results: The most important predictors of miscarriage in ART programs were detected using two models, including age, medical history of pregnancies from the particular partner, duration of stimulation, embryo quality, as well as fertilization method.

Conslusion: Research in this area, especially using ML tools for data processing makes it possible to build a software product for personalized and integrated prediction of live births for each married couple. The obtained results can optimize the state’s financial and economic expenditures to conduct ART cycles at the expense of Compulsory Health Insurance for different groups of patients. In addition, a clear and unified algorithm facilitates the targeted impact on the most probable cause of miscarriage, taking into account optimization of product preparation time and achievement of maximum effect to reduce the rate of pregnancy loss after ART.

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作者简介

Yulia Drapkina

V.I. Kulakov National Medical Research Center for Obstetrics, Gynecology and Perinatology, Ministry of Health of Russia

编辑信件的主要联系方式.
Email: yu_drapkina@oparina4.ru
ORCID iD: 0000-0002-0545-1607

PhD, Senior Researcher, Department of IVF named after Prof. B.V. Leonov

俄罗斯联邦, Moscow

Natalya Makarova

V.I. Kulakov National Medical Research Center for Obstetrics, Gynecology and Perinatology, Ministry of Health of Russia

Email: np_makarova@oparina4.ru
ORCID iD: 0000-0003-1396-7272

PhD, Leading Researcher, Department of IVF named after Prof. B.V. Leonov

俄罗斯联邦, Moscow

Andrey Kalinin

Pirogov Russian National Research Medical University, Ministry of Health of Russia

Email: zoaza8@mail.ru

student of the Faculty of Medicine

俄罗斯联邦, Moscow

Robert Vasiliev

Laboratory of Applied Artificial Intelligence Z-union

Email: yu_drapkina@oparina4.ru

Head; Vice-president of the Association of Laboratories for the Development of Artificial Intelligence; graduate student at the; Master of the Department of Applied Physics and Mathematics; Master of Economics; Bachelor’s degree

俄罗斯联邦, Moscow

Vladislav Amelin

Laboratory of Applied Artificial Intelligence Z-union

Email: yu_drapkina@oparina4.ru

Technical Director, expert in machine learning; Master’s degree (Faculty of Computational Mathematics and Cybernetics, Department of Mathematical Methods); Bachelor’s degree

俄罗斯联邦, Moscow

参考

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