Quantitative approaches in clinical reproductive endocrinology.

Voliotis, Margaritis; Hanassab, Simon; Abbara, Ali; Heinis, Thomas; Dhillo, Waljit S; Tsaneva-Atanasova, Krasimira

Voliotis, Margaritis; Hanassab, Simon; Abbara, Ali; Heinis, Thomas; Dhillo, Waljit S; Tsaneva-Atanasova, Krasimira.

Voliotis M; Department of Mathematics and Living Systems Institute, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, United Kingdom.
Hanassab S; Section of Endocrinology and Investigative Medicine, Imperial College London, London, United Kingdom.
Abbara A; Department of Computing, Imperial College London, London, United Kingdom.
Heinis T; UKRI Centre for Doctoral Training in AI for Healthcare, Imperial College London, London, United Kingdom.
Dhillo WS; Section of Endocrinology and Investigative Medicine, Imperial College London, London, United Kingdom.
Tsaneva-Atanasova K; Department of Computing, Imperial College London, London, United Kingdom.

Curr Opin Endocr Metab Res ; 27: 100421, 2022 Dec.

Article en En | MEDLINE | ID: mdl-36643692

Palabras clave

AI, artificial intelligence; AMH, anti-Müllerian hormone; ART, assisted reproductive technology; Artificial intelligence; Assisted reproductive technology; BSA, Bayesian Spectrum Analysis; Clinical decision making; E2, estradiol; FSH, follicle-stimulating hormone; GnRH, gonadotropin-releasing hormone; HA, hypothalamic amenorrhea; HPG, hypothalamic-pituitary gonadal; IVF, in vitro fertilization; In vitro fertilization; LH, luteinizing hormone; ML, machine learning; Machine learning; Mathematical modelling; OHSS, ovarian hyperstimulation syndrome; P4, progesterone; PCOS, polycystic ovary syndrome; Pulsatility analysis; Quantitative modelling; Reproductive endocrinology

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Texto completo: 1 Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Año: 2022 Tipo del documento: Article

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Texto completo: 1 Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Año: 2022 Tipo del documento: Article