An interpretable machine learning model for individualized gonadotrophin starting dose selection during ovarian stimulation.

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作者： Michael,Fanton ^[1] ; Veronica,Nutting ^[1] ; Arielle,Rothman ^[1] ; Paxton,Maeder-York ^[1] ; Eduardo,Hariton ^[2] ; Oleksii,Barash ^[3] ; Louis,Weckstein ^[3] ; Denny,Sakkas ^[4] ; Alan B,Copperman ^[5] ; Kevin,Loewke ^[6]

作者单位： Alife Health, Inc., San Francisco CA, USA. ^[1] Department of Obstetrics, Gynecology and Reproductive Sciences, University of California, San Francisco CA, USA. ^[2] Reproductive Science Center, San Ramon CA, USA. ^[3] Boston IVF - The Eugin Group, Waltham MA, USA. ^[4] Reproductive Medicine Associates of New York, New York NY, USA. ^[5] Alife Health, Inc., San Francisco CA, USA. Electronic address: kloewke@alifehealth.com. ^[6]

关键词 Artificial intelligence Gonadotrophins IVF Machine learning Ovarian stimulation

主题词回顾性研究(Retrospective Studies);精子注射, 细胞质内(Sperm Injections, Intracytoplasmic);体外受精(Fertilization in Vitro);卵泡刺激素(Follicle Stimulating Hormone);排卵诱导(Ovulation Induction);促性腺素类(Gonadotropins)

DOI 10.1016/j.rbmo.2022.07.010

PMID 36096871

发布时间 2022-12-06

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