Abstract: A knowledge management system may receive a set of data instances. The system may extract a plurality of entities from the set of data instances. The system may convert the plurality of entities into a plurality of entity embeddings, each entity embedding representing an entity in a latent space. The system may generate a reference embedding that has the same length as the plurality of entity embeddings. The system may compare, for each value in each entity embedding, the value to a corresponding value of the reference embedding. The system may generate a plurality of entity fingerprints, each entity fingerprint corresponding to an entity embedding, each entity fingerprint comprising Boolean values that are generated based on comparing values in each entity embedding to corresponding values of the reference embedding. The system may store the plurality of entity fingerprints to represent the plurality of entities.

Abstract: A data processing system implements accessing content items from a plurality of data sources, generating a knowledge graph by analyzing each of the content items with a language model to obtain embedding vectors representing each first content items, receiving a query and an indication of a format for results of the query from a first client device, generating query embeddings for the query using the first language model, searching the knowledge graph based on the query embeddings to obtain the results of the query, generating a representation of the results of the query according to the indication of the format for the results of the query, and causing the first client device to present the representation of the results of the query on a user interface of the first client device.

Abstract: A data processing system implements obtaining an image of a document and an indication of one or more content items to generate; analyzing the image of the document to generate a textual representation of contents of the document in the image; constructing a query based on the textual representation; analyzing the query using a second machine learning model to obtain embeddings representing one or more categories of information represented in the query; searching a knowledge graph based on the query embeddings to obtain results of the query; providing the query results to a content generation unit to generate the one or more content items based on the results of the query; and obtaining the one or more content items from the content generate unit.

Abstract: A data processing system implements accessing content items from a plurality of data sources, generating a knowledge graph by analyzing each of the content items with a language model to obtain embedding vectors representing each first content items, receiving a query and an indication of a format for results of the query from a first client device, generating query embeddings for the query using the first language model, searching the knowledge graph based on the query embeddings to obtain the results of the query, generating a representation of the results of the query according to the indication of the format for the results of the query, and causing the first client device to present the representation of the results of the query on a user interface of the first client device.

Abstract: A data processing system implements techniques for implementing AI-driven software modules. The data processing system automates the generation of software for analyzing complex data sets. The modules can be used to implement software modules for an information analysis and decision support platform that accurately acquires, assesses, compares, and analyzes large volumes of data across the various domains of the pharmaceutical and/or medical device spaces in a timely and efficient manner. The techniques provided herein provide utilize language models that are trained to automate the development software modules that utilize the knowledge graph. The modules are generated from natural language queries presented by a user which are analyzed and used to generate a template for the software module.

Abstract: A data processing system implements receiving a set of parameters associated with a first clinical trial, the parameters identifying one or more pharmaceuticals and/or medical conditions; identifying first documents associated with one or more second clinical trials from databases of clinical trials, new drug applications, drug label information, or a combination thereof, based on the parameters; obtaining electronic copies of the first documents; analyzing the electronic copies using a first set of models configured to identify relevant portions of the electronic copies based on a document type associated with each of the electronic copies; analyzing the relevant portions of the electronic copies using a natural language processing model to extract information; collating the information extracted from the relevant portions of the electronic copies to produce prediction information related to the first clinical trial; and analyzing the prediction information to generate one or more reports providing informati