Application of various machine learning techniques to the analysis of clinical, anamnestic, and embryological data of patients undergoing assisted reproductive technologies
- 作者: Drapkina Y.S.1, Makarova N.P.1, Vasiliev R.A.1, Amelin V.V.1, Frankevich V.E.1, Kalinina E.A.1
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隶属关系:
- Academician V.I. Kulakov National Medical Research Center for Obstetrics, Gynecology and Perinatology, Ministry of Health of the Russian Federation
- 期: 编号 3 (2024)
- 页面: 96-107
- 栏目: Original Articles
- URL: https://journals.eco-vector.com/0300-9092/article/view/631626
- DOI: https://doi.org/10.18565/aig.2023.281
- ID: 631626
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详细
Data analysis using machine learning (ML) enables more accurate and targeted identification of the most important modifiable and non-modifiable predictors of pregnancy in assisted reproductive technology (ART) programs for patients across different age groups. Predicting the performance of an ART program using ML can be achieved through various algorithms, depending on the data type and specific task at hand.
Objective: This study aimed to analyze the processing of clinical, anamnestic, and embryological data from patients undergoing ART using different ML methods. It also seeks to determine the accuracy of ART outcome prediction using various algorithms, and to select the ML model that holds the greatest practical value for predicting the onset of pregnancy.
Materials and methods: This retrospective study included 854 married couples. It analyzed data from clinical and laboratory examinations, as well as parameters of the stimulated cycle, depending on the effectiveness of the ART program using the gradient boosting algorithm over decision trees (CatBoost).
Results: Key factors that significantly influence the effectiveness of ART include the presence or absence of a history of pregnancy, the concentration of sperm in the ejaculate, and the number of embryos with arrested development. A software product based on the gradient boosting algorithm was developed to predict the individual effectiveness of the ART programs.
Conclusion: Enhancing the prediction of the effectiveness of ART programs requires better mathematical models with an integrated approach to the problem and additional markers to improve the accuracy of the software product. Constructing a model that includes not only the couple’s history but also molecular markers using ML methods will allow for the most accurate determination of the most promising groups of patients for in vitro fertilization programs, and it will increase the efficiency of ART programs by selecting the highest-quality embryos for transfer.
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作者简介
Yulia Drapkina
Academician V.I. Kulakov National Medical Research Center for Obstetrics, Gynecology and Perinatology, Ministry of Health of the Russian Federation
编辑信件的主要联系方式.
Email: yu_drapkina@oparina4.ru
ORCID iD: 0000-0002-0545-1607
PhD, Researcher at the Department of IVF named after Prof. B.V. Leonov
俄罗斯联邦, MoscowNatalya Makarova
Academician V.I. Kulakov National Medical Research Center for Obstetrics, Gynecology and Perinatology, Ministry of Health of the Russian Federation
Email: np_makarova@oparina4.ru
PhD, Leading Researcher at the Department of IVF named after Prof. B.V. Leonov
俄罗斯联邦, MoscowRobert Vasiliev
Academician V.I. Kulakov National Medical Research Center for Obstetrics, Gynecology and Perinatology, Ministry of Health of the Russian Federation
Email: aig@oparina4.ru
Head of the Laboratory of Applied Artificial Intelligence Z-union, Vice-President of the Association of Laboratories for the Development of Artificial Intelligence, graduate student at the Moscow Institute of Physics and Technology (MIPT), Master of the Department of Applied Physics and Mathematics of the Moscow Institute of Physics and Technology, Master of Economics, Bachelor’s degree at the Research University «Moscow Institute of Electronic Technology»
俄罗斯联邦, MoscowVladislav Amelin
Academician V.I. Kulakov National Medical Research Center for Obstetrics, Gynecology and Perinatology, Ministry of Health of the Russian Federation
Email: aig@oparina4.ru
Technical Director of the Laboratory of Applied Artificial Intelligence Z-union, expert in machine learning, Master’s degree from Moscow State University (Faculty of Computational Mathematics and Cybernetics, Department of Mathematical Methods), Bachelor’s degree from the National Research University «Moscow Institute of Electronic Technology»
俄罗斯联邦, MoscowVladimir Frankevich
Academician V.I. Kulakov National Medical Research Center for Obstetrics, Gynecology and Perinatology, Ministry of Health of the Russian Federation
Email: v_vfrankevich@oparina4.ru
ORCID iD: 0000-0002-9780-4579
Dr. Sci. (Physical and Mathematical Sciences), Deputy Director of the Institute of Translational Medicine
俄罗斯联邦, MoscowElena Kalinina
Academician V.I. Kulakov National Medical Research Center for Obstetrics, Gynecology and Perinatology, Ministry of Health of the Russian Federation
Email: e_kalinina@oparina4.ru
ORCID iD: 0000-0002-8922-2878
Dr. Med. Sci., Professor, Head of the Department of IVF named after Prof. B.V. Leonov
俄罗斯联邦, Moscow参考
- Гусев А.В., Новицкий Р.Э., Ившин А.А., Алексеев А.А. Машинное обучение на лабораторных данных для прогнозирования заболеваний. ФАРМАКОЭКОНОМИКА. Современная фармакоэкономика и фармакоэпидемиология. 2021; 14(4): 581-92. [Gusev A.V., Novitskii R.E., Ivshin A.A., Alekseev A.A. Machine learning based on laboratory data for disease prediction. FARMAKOEKONOMIKA. Modern Pharmacoeconomics and Pharmacoepidemiology. 2021; 14(4): 581-92. (in Russian)]. https://dx.doi.org/10.17749/2070-4909/farmakoekonomika.2021.115.
- Драпкина Ю.С., Калинина Е.А., Макарова Н.П., Мильчаков К.С., Франкевич В.Е. Искусственный интеллект в репродуктивной медицине: этические и клинические аспекты. Акушерство и гинекология. 2022; 11: 37-44. [Drapkina Yu.S., Kalinina E.A., Makarova N.P., Mil’chakov K.S., Frankevich V.E. Artificial intelligence in reproductive medicine: ethical and clinical aspects. Obstetrics and Gynecology. 2022; (11): 37-44 (in Russian)]. https://dx.doi.org/10.18565/aig.2022.11.37-44.
- Сахибгареева М.В., Заозерский А.Ю. Разработка системы прогнозирования диагнозов заболеваний на основе искусственного интеллекта. Вестник РГМУ. 2017; 6: 42-6. [Sakhibgareeva M.V., Zaozerskii A.Yu. Developing an artificial intelligence-based system for medical prediction. Vestnik RGMU. 2017; (6): 42-6. (in Russian)].
- Ившин А.А., Багаудин Т.З., Гусев А.В. Искусственный интеллект на страже репродуктивного здоровья. Акушерство и гинекология. 2021; 5: 17-24. [Ivshin A.A., Bagaudin T.Z., Gusev A.V. Artificial intelligence on guard of reproductive health. Obstetrics and Gynecology. 2021; (5): 17-24. (in Russian)]. https://dx.doi.org/10.18565/aig.2021.5.17-24.
- Шелякин В.А., Губарева И.Д., Кокшарова Н.Г. О методике расчета дифференцированных подушевых нормативов финансового обеспечения обязательного медицинского страхования. Бюллетень Национального научно-исследовательского института общественного здоровья имени Н.А. Семашко. 2012; 5: 152-6. [Shelyakin V.A., Gubareva I.D., Koksharova N.G. On the method of calculating differentiated per capita standards for financial support of compulsory health insurance. Bulletin of the National Public Health Research Institute named after N.A. Semashko. 2012; (5): 152-6. (in Russian)].
- Raef B., Ferdousi R. A review of machine learning approaches in assisted reproductive technologies. Acta Inform. Med. 2019; 27(3): 205-11. https://dx.doi.org/10.5455/aim.2019.27.205-211.
- Elhazmi A., Al-Omari A., Sallam H., Mufti H.N., Rabie A.A., Alshahrani M. et al. Machine learning decision tree algorithm role for predicting mortality in critically ill adult COVID-19 patients admitted to the ICU. J. Infect. Public Health. 2022; 15(7): 826-34. https://dx.doi.org/10.1016/j.jiph.2022.06.008.
- Barberis E., Khoso S., Sica A., Falasca M., Gennari A., Dondero F. et al. Precision medicine approaches with metabolomics and artificial intelligence. Int. J. Mol. Sci. 2022; 23(19): 11269. https://dx.doi.org/10.3390/ijms231911269.
- Драпкина Ю.С., Макарова Н.П., Татаурова П.Д., Калинина Е.A. Поддержка врачебных решений с помощью глубокого машинного обучения при лечении бесплодия методами вспомогательных репродуктивных технологий. Медицинский Совет. 2023; 15: 27-37. [Drapkina Yu.S., Makarova N.Р., Tataurova P.D., Kalinina E.A. Deep machine learning applied to support clinical decision-making in the treatment of infertility using assisted reproductive technologies. Medical Council. 2023; (15): 27-37. (in Russian)]. https://dx.doi.org/10.21518/ms2023-368.
- Salditt M., Humberg S., Nestler S. Gradient tree boosting for hierarchical data. Multivariate Behav. Res. 2023; 58(5): 911-37. https://dx.doi.org/ 10.1080/00273171.2022.2146638.
- Wang C.W., Kuo C.Y., Chen C.H., Hsieh Y.H., Su E.C. Predicting clinical pregnancy using clinical features and machine learning algorithms in in vitro fertilization. PLOS One. 2022; 17(6): e0267554. https://dx.doi.org/10.1371/journal.pone.0267554.
- Al-Shehari T., Alsowail R.A. An insider data leakage detection using one-hot encoding, synthetic minority oversampling and machine learning techniques. Entropy (Basel). 2021; 23(10): 1258. https://dx.doi.org/10.3390/ e23101258.
- Акжолов Р.К. Машинное обучение. Вестник науки. 2019; 3(6): 348-51. [Akzholov R.K. Machine learning. Vestnik Nauki. 2019; 3(6): 348-51. (in Russian)].
- Hancock J.T., Khoshgoftaar T.M. CatBoost for big data: an interdisciplinary review. J. Big Data. 2020; 7(1): 94. https://dx.doi.org/10.1186/ s40537-020-00369-8.
- Бучацкая В.В., Бучацкий П.Ю., Лобанов В.Е. Анализ алгоритмов прогнозирования. Вестник Адыгейского государственного университета. Серия 4: Естественно-математические и технические науки. 2020; 4: 49-52. [Buchatskaya V.V., Buchatskii P.Yu., Lobanov V.E. Analysis of forecasting algorithms. Bulletin of Adygea State University. Series 4: Natural-mathematical and technical sciences. 2020; (4): 49-52. (in Russian)].
- XGBoost. Университет ИТМО. Электронные текстовые данные. Доступно по: https://neerc.ifmo.ru/wiki/index.php?title=XGBoost [Дата обращения: 24.10.2021]. [XGBoost. Universitet ITMO. Elektronnye tekstovye dannye. Available at: https://neerc.ifmo.ru/wiki/index.php?title=XGBoost [Accessed 24.10.2021]. (in Russian)].
- Ghosh D., Cabrera J. Enriched random forest for high dimensional genomic data. IEEE/ACM Trans. Comput. Biol. Bioinform. 2022; 19(5): 2817-28. https://dx.doi.org/10.1109/TCBB.2021.3089417.
- Hu J., Szymczak S. A review on longitudinal data analysis with random forest. Brief. Bioinform. 2023; 24(2): bbad002. https://dx.doi.org/10.1093/bib/bbad002.
- Ganaie M.A., Tanveer M., Suganthan P.N., Snasel V. Oblique and rotation double random forest. Neural Netw. 2022; 153: 496-517. https://dx.doi.org/10.1016/ j.neunet.2022.06.012.
- Tikhaeva K., Nesterova N., Tomilov E., Sotkin S., Muhina A., Zavyalov P., Rosyuk E. Gradient boosting predictive model of ovarian response for hormonal therapy in infertility treatment. Russian Advances in Fuzzy Systems and Soft Computing: Selected Contributions to the 10th International Conference «Integrated Models and Soft Computing in Artificial Intelligence» (IMSC-2021), May 17-20, 2021, Kolomna, Russian Federation.
- Yiğit P., Bener A., Karabulut S. Comparison of machine learning classification techniques to predict implantation success in an IVF treatment cycle. Reprod. Biomed. Online. 2022; 45(5): 923-34. https://dx.doi.org/10.1016/ j.rbmo.2022.06.022.
- Hansen K.R., He A.L., Styer A.K., Wild R.A., Butts S., Engmann L. et al. Predictors of pregnancy and live-birth in couples with unexplained infertility after ovarian stimulation-intrauterine insemination. Fertil. Steril. 2016; 105(6): 1575-83.e2. https://dx.doi.org/10.1016/j.fertnstert.2016.02.020.
- Meijerink A.M., Cissen M., Mochtar M.H., Fleischer K., Thoonen I., de Melker A.A. et al. Prediction model for live birth in ICSI using testicular extracted sperm. Hum. Reprod. 2016; 31(9): 1942-51. https://dx.doi. org/10.1093/humrep/dew146.
- Blank C., Wildeboer R.R., DeCroo I., Tilleman K., Weyers B., de Sutter P. et al. Prediction of implantation after blastocyst transfer in in vitro fertilization: a machine-learning perspective. Fertil. Steril, 2019; 111(2): 318-26. https://dx.doi.org/10.1016/j.fertnstert.2018.10.030.
- Qiu J., Li P., Dong M., Xin X., Tan J. Personalized prediction of live birth prior to the first in vitro fertilization treatment: a machine learning method. J. Transl. Med. 2019; 17(1): 317. https://dx.doi.org/10.1186/s12967-019-2062-5.
- Rodrigo L., Meseguer M., Mateu E., Mercader A., Peinado V., Bori L. et al. Sperm chromosomal abnormalities and their contribution to human embryo aneuploidy. Biol. Reprod. 2019; 101(6): 1091-101. https://dx.doi.org/10.1093/biolre/ioz125.
- Harris A.L., Vanegas J.C., Hariton E., Bortoletto P., Palmor M., Humphries L.A. et al. Semen parameters on the day of oocyte retrieval predict low fertilization during conventional insemination IVF cycles. J. Assist. Reprod. Genet. 2019; 36(2): 291-8. https://dx.doi.org/10.1007/s10815-018-1336-9.
- McCoy R.C., Summers M.C., McCollin A., Ottolini C.S., Ahuja K., Handyside A.H. Meiotic and mitotic aneuploidies drive arrest of in vitro fertilized human preimplantation embryos. Genome Med. 2023; 15(1): 77. https://dx.doi.org/10.1186/s13073-023-01231-1.
- Долудин Ю.В., Драпкина Ю.С., Сазонкина П.О., Киселев А.Р., Горбунов К.С. Виртуальная система хранения биологических образцов и ассоциированных данных. Свидетельство о государственной регистрации программы для ЭВМ. Номер свидетельства: RU 2023610092. Патентное ведомство: Россия. Год публикации: 2023. Номер заявки: 2022686282. Дата регистрации: 19.12.2022. [Doludin Yu.V., Drapkina Yu.S., Sazonkina P.O., Kiselev A.R., Gorbunov K.S. Virtual storage system for biological samples and associated data. Certificate of state registration of a computer program. Certificate number: RU 2023610092. Patent Office: Russia. Year of publication: 2023. Application number: 2022686282. Registration date: 19/12/2022. (in Russian)].
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