Abstract: Described herein are systems and methods for modeling complex outcomes using clustering and machine learning algorithms. Machine learning algorithms and models can be implemented on platforms comprising one or more user interfaces and an insight engine. In these embodiments, insight engine comprises a machine learning software algorithm (or module) configured to ingest data and generate insights.

Abstract: Described herein are systems and methods for modeling complex outcomes using clustering and machine learning algorithms. Machine learning algorithms and models can be implemented on platforms comprising one or more user interfaces and an insight engine. In these embodiments, insight engine comprises a machine learning software algorithm (or module) configured to ingest data and generate insights.

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 relates to compositions and methods for molecular profiling and diagnostics for genetic disorders and cancer, including but not limited to gene expression product markers associated with cancer or genetic disorders. In particular, the present invention provides algorithms and methods of classifying cancer, for example, thyroid cancer, methods of determining molecular profiles, and methods of analyzing results to provide a diagnosis.

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: The disclosure in some aspects provides methods of determining the likelihood that a subject has lung cancer based on the expression of informative-genes. In other aspects, the disclosure provides methods for determining an appropriate diagnostic intervention plan for a subject based on the expression of informative-genes. Related compositions and kits are provided in other aspects of the disclosure.

Abstract: Described herein are systems and methods for modeling complex outcomes using clustering and machine learning algorithms. Machine learning algorithms and models can be implemented on platforms comprising one or more user interfaces and an insight engine. In these embodiments, insight engine comprises a machine learning software algorithm (or module) configured to ingest data and generate insights.

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: Methods for assessing infertility and related pathologies and informing treatment type and timing thereof are provided. According to certain embodiments, methods of the invention include determining levels of one or more transcripts present in a sample obtained from a subject suspected of having endometriosis, identifying transcript levels that correspond to a regulation pattern specific to a time-point in a uterine cycle, and characterizing endometriosis of the subject based upon the identified transcript levels. The invention includes methods for assessing age-associated increase in aneuploidy rates based on FSH levels and IVF success rates based on obesity in PCOS patients.

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: Methods for assessing infertility and related pathologies and informing treatment type and timing thereof are provided. According to certain embodiments, methods of the invention include determining levels of one or more transcripts present in a sample obtained from a subject suspected of having endometriosis, identifying transcript levels that correspond to a regulation pattern specific to a time-point in a uterine cycle, and characterizing endometriosis of the subject based upon the identified transcript levels. The invention includes methods for assessing age-associated increase in aneuploidy rates based on FSH levels and IVF success rates based on obesity in PCOS patients.

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: 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: The invention generally relates to methods and devices for assessing risk of female infertility. In certain aspects, methods of the invention involve obtaining a sample, conducting an assay on at least one infertility-associated biomarker, and assessing risk to the patient of developing early-onset decrease in fertility based upon results of the assay.

Abstract: A system for determining relative operational performance in a clinical trial may include a processor, a data comparator and visualizer, and a graphical user interface. The processor filters received clinical data into multiple metric criteria to generate multiple metric data sets and filters each metric data set into a respective industry data set and a respective candidate data set. The processor then calculates statistical measures for each industry data set and candidate data set and transforms each industry data set and candidate data set based on each data set's respective statistical measures. The data comparator and visualizer compares each transformed candidate data set to the transformed industry data set for the respective metric criterion to determine a candidate percentile for each metric criterion. The graphical user interface displays the candidate percentiles for the metric criteria. A method for determining relative operational performance in a clinical trial is also described.