Интеллектуальная система поддержки принятия врачебных решений для прогнозирования исхода протокола вспомогательных репродуктивных технологий на различных этапах его проведения
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Научный журнал Моделирование, оптимизация и информационные технологииThe scientific journal Modeling, Optimization and Information Technology
Online media
issn 2310-6018

An intelligent clinical decision support system for predicting the outcome of an assisted reproductive technology protocol at various stages of its implementation

idSinotova S.L. idSolodushkin S.I. idPlaksina A.N. idMakutina V.A.

UDC 519.688, 004.891
DOI: 10.26102/2310-6018/2022.37.2.009

  • Abstract
  • List of references
  • About authors

The article describes the logic of an intelligent clinical decision support system (CDSS), which is based on a set of machine learning models that allow predicting the outcome of an assisted reproductive technologies (ART) protocol at various stages of its implementation. To create all the prognostic models, data from the register of ART protocols, which enables tracing the influence of the woman's history and the course of the protocol on the health of the child from birth to three years of age, were used. The outcome of the ART protocol is expressed in the likelihood of pregnancy, the most common complications of its course, such as isthmic-cervical insufficiency, arterial hypertension, placenta previa, gestational diabetes mellitus, disturbances in the amount of amniotic fluid and premature rupture of the membranes, in a term and method of delivery, as well as in the state of health of the born child for three years. The impact of predicted pregnancy complications on the outcome of childbirth as well as the impact of predicted pregnancy complications, the date and method of delivery on the health of the born child, described in the health group and the predicted group of ICD-10 diagnoses, are taken into consideration. The CDSS is provided for in vitro fertilization protocols, including those using intracytoplasmic spermatozoa injection into the oocyte (IVF/ISKI) and cryotransfer. The CDSS contains 77 predictive models, of which 72 models are binary classifiers, 5 are regression models. Random Forest Algorithm was employed to create all machine learning models. The ROC-AUC value of the binary classifiers of the system is 0.936 95% CI [0.914; 0.958], the accuracy of binary classifiers is 0.897 95% CI [0.880; 0.915], F-test for regression models does not refute the model adequacy hypothesis. The application of such a system will make it possible to obtain an objective assessment drawing on a large amount of data, which is of particular interest for specialists in the field of ART, and to visually demonstrate to the clients of ART centers the main stages of the upcoming process.

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Sinotova Svetlana Leonidovna

ORCID |

UrFU named after the first President of Russia B.N.Yeltsin

Ekaterinburg, Russian Federation

Solodushkin Svyatoslav Igorevich
Candidate of Physical and Mathematical Sciences

ORCID |

UrFU named after the first President of Russia B.N.Yeltsin

Ekaterinburg, Russian Federation

Plaksina Anna Nikolaevna
Doctor of Medical Sciences

ORCID |

USMU of the Ministry of Health of the Russian Federation

Ekaterinburg, Russian Federation

Makutina Valerija Andreevna
Candidate of Biological Sciences

ORCID |

The Family Medicine Centre

Ekaterinburg, Russian Federation

Keywords: machine learning, clinical decision support system, assisted reproductive technologies, predictive models, software application, child health prediction

For citation: Sinotova S.L. Solodushkin S.I. Plaksina A.N. Makutina V.A. An intelligent clinical decision support system for predicting the outcome of an assisted reproductive technology protocol at various stages of its implementation. Modeling, Optimization and Information Technology. 2022;10(2). Available from: https://moitvivt.ru/ru/journal/pdf?id=1169 DOI: 10.26102/2310-6018/2022.37.2.009 (In Russ).

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Full text in PDF

Received 20.04.2022

Revised 04.05.2022

Accepted 17.05.2022

Published 19.05.2022