Abstract: Methods and systems are disclosed herein for using artificial intelligence to determine which standardized text description an adverse event reported by a patient may match with. Artificial intelligence/machine learning may be used to determine matches between standardized text descriptions of adverse events and other text descriptions of adverse events (e.g., text descriptions input by patients that have taken a drug). Techniques described herein may improve the functioning of a computing system by allowing it to perform an action that it otherwise could not perform (e.g., determining a standardized text description for an adverse event experienced by a patient).

Abstract: Systems and methods are described for operationalizing AI models using web-based user interfaces. The system may receive a first user input uploading of a software container, wherein the software container comprises a data model written in a first programming language. The system may generate for display, in a user interface of a web application, a prediction based on the data model, wherein the web application is written in a second programming language, and wherein the web application is configured to receive selection of the parameter and execute, in the software container, the data model.

Abstract: The present disclosure relates to an ensemble network of natural language processing (NLP) models configured to determine how well a given document addresses one or more requirements set forth in a requirement-specifying document. The NLP models may extract relevant text from the documents and perform term-similarity measurements to determine how similar the text tokens from one document are to the other and generate a similarity score for each sentence and each section of each document. The similarity scores may then be used to determine whether the response document addresses the requirements. If the response document does not address particular requirements, then data flags may be generated to indicate that a corresponding section of the response document may need to be updated.

Abstract: Systems and methods are described for operationalizing AI models using web-based user interfaces. The system may receive a first user input uploading of a software container, wherein the software container comprises a data model written in a first programming language. The system may generate for display, in a user interface of a web application, a prediction based on the data model, wherein the web application is written in a second programming language, and wherein the web application is configured to receive selection of the parameter and execute, in the software container, the data model.