Abstract: An apparatus for facilitating development of software to use a neural network in generating output indicative of a near real-time prediction of clinical study enrollment, comprises one or more processors; and one or more hardware storage devices storing instructions that are executable by the one or more processors to perform one or more first operations comprising: receiving, in a development environment, one or more instructions that are executable by the one or more processors to perform one or more second operations comprising: receiving, from the one or more user interfaces, input data corresponding to data input into the one or more portions of the user interface; inputting, into a neural network, the target schema, received input data specifying the given clinical trial, the enrollment data and the historical data; and receiving, from the neural network, prediction data specifying one or more near real-time predictions of enrollment for the clinical trial.

Abstract: Detecting serious adverse events from Electronic Data Capture (EDC) data for clinical trials. A method includes extracting clinical data from EDC data of a plurality of clinical trials, the clinical data comprising form names and field names. The clinical data is standardized across the plurality of clinical trials to generate subject-level data and adverse event data. The adverse event data is used to produce standardized adverse events. A serious adverse event model is trained by inputting the standardized adverse events, trial-level features, and the subject-level data. The trained serious adverse event model is used to determine a probability that a standardized adverse event is a serious adverse event.

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.