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: An apparatus for performing a remote test of range of motion of a person operating a user device includes a transceiver, a processor, and a display. The transceiver is configured to transmit a link to the user device and to receive motion data from the user device. The processor is configured to calculate in real time, based on the motion data, the position of the user device to enable real-time display to a test provider of the performance of the test and to determine in real time the quality of the test. The display is configured to show in real time a continuous indication of the performance of the test and quality results of the test. A method for performing a remote test of range of motion of a person operating a user device is also described and claimed.

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: An apparatus for automatically mapping a verbatim narrative to a term in a medical terminology dictionary includes a natural language processor and a comparator. The natural language processor processes terms from the medical terminology dictionary and from a medical coding decision database to generate a processed database that also includes the original terms from the medical terminology dictionary and the medical coding decision database. The natural language processor also processes the verbatim narrative. The comparator compares the processed verbatim narrative to the terms in the processed database and determines whether the processed verbatim narrative is an exact match to a term in the processed database. The verbatim narrative is mapped to the term in the medical terminology dictionary that corresponds to the term in the processed database that is an exact match. The verbatim narratives may include adverse event narratives, concomitant medication narratives, or other types of narratives.

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: An apparatus for performing a remote test of range of motion of a person operating a user device includes a transceiver, a processor, and a display. The transceiver is configured to transmit a link to the user device and to receive motion data from the user device. The processor is configured to calculate in real time, based on the motion data, the position of the user device to enable real-time display to a test provider of the performance of the test and to determine in real time the quality of the test. The display is configured to show in real time a continuous indication of the performance of the test and quality results of the test. A method for performing a remote test of range of motion of a person operating a user device is also described and claimed.

Abstract: An apparatus for automatically mapping a verbatim narrative to a term in a medical terminology dictionary includes a natural language processor and a comparator. The natural language processor processes terms from the medical terminology dictionary and from a medical coding decision database to generate a processed database that also includes the original terms from the medical terminology dictionary and the medical coding decision database. The natural language processor also processes the verbatim narrative. The comparator compares the processed verbatim narrative to the terms in the processed database and determines whether the processed verbatim narrative is an exact match to a term in the processed database. The verbatim narrative is mapped to the term in the medical terminology dictionary that corresponds to the term in the processed database that is an exact match. The verbatim narratives may include adverse event narratives, concomitant medication narratives, or other types of narratives.