Abstract: A system for predicting subject enrollment for a study includes a time-to-first-enrollment (TTFE) model and a first-enrollment-to-last-enrollment (FELE) model for each site in the study. The TTFE model includes a Gaussian distribution with a generalized linear mixed effects model solved with maximum likelihood point estimation or with Bayesian regression, and the FELE model includes a negative binomial distribution with a generalized linear mixed effects model solved with maximum likelihood point estimation or with Bayesian regression estimation.

Abstract: A system for predicting subject enrollment for a study includes a time-to-first-enrollment (TTFE) model and a first-enrollment-to-last-enrollment (FELE) model for each site in the study. The TTFE model includes a Gaussian distribution with a generalized linear mixed effects model solved with maximum likelihood point estimation or with Bayesian regression, and the FELE model includes a negative binomial distribution with a generalized linear mixed effects model solved with maximum likelihood point estimation or with Bayesian regression estimation.

Abstract: A system for developing a model to automatically determine the probability that a serious adverse event occurred during a clinical trial includes a clinical data standardizer, a data processor, and a model developer. The clinical data standardizer receives clinical trial data and standardizes the clinical trial data and form and field names across clinical trials. The data processor generates standardized adverse event terms from the standardized data and form and field names. The model developer merges the standardized adverse event terms and other adverse event data, demographic information, and trial features and develops a serious adverse event (SAE) machine learning model.

Abstract: The present invention generally relates to systems and methods for determining the probability of a pregnancy at a selected point in time. Systems and methods of the invention employ an algorithm that has been trained on a reference set of data from a plurality of women for whom at least one of fertility-associated phenotypic traits, fertility-associated medical interventions, or pregnancy outcomes are known, in which the algorithm accounts for any woman who ceases pregnancy attempts prior to reaching a live birth outcome.

Abstract: A system for predicting subject enrollment for a study includes a time-to-first-enrollment (TTFE) model and a first-enrollment-to-last-enrollment (FELE) model for each site in the study. The TTFE model includes a Gaussian distribution with a generalized linear mixed effects model solved with maximum likelihood point estimation or with Bayesian regression, and the FELE model includes a negative binomial distribution with a generalized linear mixed effects model solved with maximum likelihood point estimation or with Bayesian regression estimation.

Abstract: The present invention generally relates to systems and methods for determining the probability of a pregnancy at a selected point in time. Systems and methods of the invention employ an algorithm that has been trained on a reference set of data from a plurality of women for whom at least one of fertility-associated phenotypic traits, fertility-associated medical interventions, or pregnancy outcomes are known, in which the algorithm accounts for any woman who ceases pregnancy attempts prior to reaching a live birth outcome.

Abstract: The present invention generally relates to systems and methods for determining the probability of a pregnancy at a selected point in time. Systems and methods of the invention employ an algorithm that has been trained on a reference set of data from a plurality of women for whom at least one of fertility-associated phenotypic traits, fertility-associated medical interventions, or pregnancy outcomes are known, in which the algorithm accounts for any woman who ceases pregnancy attempts prior to reaching a live birth outcome.

Abstract: The present invention generally relates to systems and methods for determining the probability of a pregnancy at a selected point in time. Systems and methods of the invention employ an algorithm that has been trained on a reference set of data from a plurality of women for whom at least one of fertility-associated phenotypic traits, fertility-associated medical interventions, or pregnancy outcomes are known, in which the algorithm accounts for any woman who ceases pregnancy attempts prior to reaching a live birth outcome.

Abstract: The present invention generally relates to systems and methods for determining the probability of a pregnancy at a selected point in time. Systems and methods of the invention employ an algorithm that has been trained on a reference set of data from a plurality of women for whom at least one of fertility-associated phenotypic traits, fertility-associated medical interventions, or pregnancy outcomes are known, in which the algorithm accounts for any woman who ceases pregnancy attempts prior to reaching a live birth outcome.

Abstract: The present invention generally relates to systems and methods for determining the probability of a pregnancy at a selected point in time. Systems and methods of the invention employ an algorithm that has been trained on a reference set of data from a plurality of women for whom at least one of fertility-associated phenotypic traits, fertility-associated medical interventions, or pregnancy outcomes are known, in which the algorithm accounts for any woman who ceases pregnancy attempts prior to reaching a live birth outcome.

Abstract: The present invention generally relates to systems and methods for determining the probability of a pregnancy at a selected point in time. Systems and methods of the invention employ an algorithm that has been trained on a reference set of data from a plurality of women for whom at least one of fertility-associated phenotypic traits, fertility-associated medical interventions, or pregnancy outcomes are known, in which the algorithm accounts for any woman who ceases pregnancy attempts prior to reaching a live birth outcome.

Abstract: The present invention generally relates to systems and methods for determining the probability of a pregnancy at a selected point in time. Systems and methods of the invention employ an algorithm that has been trained on a reference set of data from a plurality of women for whom at least one of fertility-associated phenotypic traits, fertility-associated medical interventions, or pregnancy outcomes are known, in which the algorithm accounts for any woman who ceases pregnancy attempts prior to reaching a live birth outcome.