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May 16, 2022Fertility and SterilityOpen Access

An interpretable machine learning model for predicting the optimal day of trigger during ovarian stimulation

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To develop and evaluate an interpretable machine learning model for predicting the optimal day of trigger administration during ovarian stimulation.
Designed an interpretable machine learning framework to assist decision-making regarding trigger timing during ovarian stimulation for in vitro fertilization.
Demonstrated the feasibility of developing an interpretable machine learning model to accurately predict the optimal day of trigger.
Identified that implementing the predictive model has the qualitative potential to improve reproductive outcomes in in vitro fertilization cohorts.

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Implication

Machine learning model demonstrates feasible trigger day optimization in ovarian stimulation patients, suggesting improved outcomes during in vitro fertilization.

Cite This Study

Fanton et al. (2022) studied this question.

synapsesocial.com/papers/69d695a1c5b08eef10029c4c https://doi.org/10.1016/j.fertnstert.2022.04.003

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Implication

Machine learning model demonstrates feasible trigger day optimization in ovarian stimulation patients, suggesting improved outcomes during in vitro fertilization.

An interpretable machine learning model for predicting the optimal day of trigger during ovarian stimulation

Key Points

Discussion

Implication

Cite This Study

An interpretable machine learning model for predicting the optimal day of trigger during ovarian stimulation

Key Points

Discussion

Implication

Cite This Study

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