Machine learning vs. classic statistics for the prediction of IVF outcomes-Reference-Cited by-同舟云学术

Machine learning vs. classic statistics for the prediction of IVF outcomes

Published:2020-08-11 Issue:10 Volume:37 Page:2405-2412
ISSN:1058-0468
Container-title:Journal of Assisted Reproduction and Genetics
language:en
Short-container-title:J Assist Reprod Genet

Author:

Barnett-Itzhaki Zohar^ORCID,Elbaz Miriam,Butterman Rachely,Amar Devora,Amitay Moshe,Racowsky Catherine,Orvieto Raoul,Hauser Russ,Baccarelli Andrea A.,Machtinger Ronit

Funder

National Institutes of Health

Environment and Health Fund

Publisher

Springer Science and Business Media LLC

Subject

Genetics (clinical),Developmental Biology,Obstetrics and Gynecology,Genetics,Reproductive Medicine,General Medicine

Link

https://link.springer.com/content/pdf/10.1007/s10815-020-01908-1.pdf

Reference18 articles.

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3. Jovic S, Miljkovic M, Ivanovic M, Saranovic M, Arsic M. Prostate cancer probability prediction by machine learning technique. Cancer Invest. 2017;35(10):647–51.

4. Obermeyer Z, Emanuel EJ. Predicting the future - big data, machine learning, and clinical medicine. N Engl J Med. 2016;375(13):1216–9.

5. Blank C, Wildeboer RR, 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.

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