Abstract: A system for predicting the occurrence of an event includes an event detector and a reporting processor. The event detector is configured to: receive data that defines a plurality of social media items; receive a real-time data feed; and predict the occurrence of an event based on a correlation between information in the plurality of social media items and activity associated with the real-time data feed. The reporting processor is configured to determine an event type associated with the event; identify a sentiment of the predicted event based on historical data in the real-time data feed, and generate a recommendation for preventing the occurrence of the event based on at least one of the event type and the sentiment of the predicted event. The recommendation includes a plurality of actions. The reporting processor is coupled to a knowledge graph database that corresponds to an ontology that defines one or more relationships between event types, and response types.

Abstract: The methods and systems may automatically generate criteria for the different sections of the protocol document for a clinical study. The methods and systems use machine learning models to identify medical articles that are associated with the clinical study of a protocol document. The machine learning models analyze the medical articles and generate recommended criteria for the different sections of the protocol document based on the analysis.

Abstract: The methods and systems may improve the development of protocol documents used for clinical trials. The methods and systems may automatically estimate the likelihood of success or failure of executing a protocol document for a clinical study using a machine learning model that leverages several hundred thousand of past protocol documents and the outcomes of the clinical studies. The methods and systems may highlight sections of the protocol document that may increase a likelihood of an unsuccessful execution of the protocol document and may provide one or more recommendations to improve the highlighted sections of the protocol document.

Abstract: A relevance system ranks a set of medical studies based on a relevance of each medical study in the set of medical studies to a patient profile. The relevance system includes a relevance model. The relevance model determines a relevance of each medical study to the patient profile based on a semantic relationship score, a concept relationship score, and a term-occurrence score. The semantic relationship score is a measure of a similarity in semantic meaning of a medical study and a patient profile. The concept relationship score is a measure of the closeness of medical concepts in a medical study to medical concepts in a patient profile. The term-occurrence score is a measure of occurrences of terms in a medical study that also appear in a patient profile and the statistical significances of the terms.

Abstract: A device may generate a product delivery map that includes route information that is to be used by a fleet of delivery vehicles for performing a set of deliveries, and a set of location constraints identifying locations that are to be avoided by the fleet of delivery vehicles when performing the set of deliveries. The device may generate a collaborative interactions map that includes a set of collaborative constraints indicating particular supplier organizations that are candidates to engage in collaborative logistics. The device may determine, based on the set of location constraints and the set of collaborative constraints, a set of delivery schedules that are to be used to perform the set of deliveries. The device may provide the set of delivery schedules to one or more devices associated with the delivery organization to allow the fleet of delivery vehicles to perform the set of deliveries.

Abstract: A system for determining adverse effects associated with a substance includes a side-effect recognizer, a relationship extractor, a processor, and a reporting system. The side-effect recognizer utilizes a first recurrent neural network (RNN) to identify a first portion of received data associated with an adverse effect to thereby determine the adverse effect associated with the received data. The relationship extractor utilizes a second RNN to identify a second portion of the received data associated with a substance and a third portion of the received data that indicates a relationship between the substance and the adverse effect to thereby determine the substance associated with the received data and the relationship between the substance and the adverse effect. The processor is in communication with the adverse effect recognizer and the substance relationship extractor and aggregates and relates the adverse effect, substance, and relationship.

Abstract: A device may generate a product delivery map that includes route information that is to be used by a fleet of delivery vehicles for performing a set of deliveries, and a set of location constraints identifying locations that are to be avoided by the fleet of delivery vehicles when performing the set of deliveries. The device may generate a collaborative interactions map that includes a set of collaborative constraints indicating particular supplier organizations that are candidates to engage in collaborative logistics. The device may determine, based on the set of location constraints and the set of collaborative constraints, a set of delivery schedules that are to be used to perform the set of deliveries. The device may provide the set of delivery schedules to one or more devices associated with the delivery organization to allow the fleet of delivery vehicles to perform the set of deliveries.

Abstract: A device may receive multimedia data, metadata, and/or policy data. The device may process the policy data using a first set of techniques to determine a first set of embeddings for the policy data. The device may process the multimedia data or the metadata using a second set of techniques to determine a second set of embeddings for the multimedia data or the metadata. The device may process the first set of embeddings and the second set of embeddings using a knowledge graph to determine whether the multimedia content or the access by the user violates the policy. The device may perform an action based on a result of processing the first set of embeddings and the second set of embeddings. The action may relate to the multimedia content or the access to the multimedia content by the user.

Abstract: Systems and methods for identifying an adverse effect of a pharmaceutical substance are provided. A system may tokenize an electronic medical record for a plurality of tokens. The system may further generate a distance score between a first vector of a word embedding model and a second vector of the word embedding model. The first vector may correspond to a substance token and the second vector may correspond to a condition token. The system may further detect an absence of a preventative association between the substance token and the condition token in a prevention repository. The system may further generate a causal link indication indicative of a causal link between the substance token and the condition token in response to the distance score being greater than a threshold value and detection of the absence of the preventative association between the substance token and the condition token.

Abstract: A system for predicting the occurrence of an event includes an event detector and a reporting processor. The event detector is configured to: receive data that defines a plurality of social media items; receive a real-time data feed; and predict the occurrence of an event based on a correlation between information in the plurality of social media items and activity associated with the real-time data feed. The reporting processor is configured to determine an event type associated with the event; identify a sentiment of the predicted event based on historical data in the real-time data feed, and generate a recommendation for preventing the occurrence of the event based on at least one of the event type and the sentiment of the predicted event. The recommendation includes a plurality of actions. The reporting processor is coupled to a knowledge graph database that corresponds to an ontology that defines one or more relationships between event types, and response types.

Abstract: Systems and methods for identifying an adverse effect of a pharmaceutical substance are provided. A system may tokenize an electronic medical record for a plurality of tokens. The system may further generate a distance score between a first vector of a word embedding model and a second vector of the word embedding model. The first vector may correspond to a substance token and the second vector may correspond to a condition token. The system may further detect an absence of a preventative association between the substance token and the condition token in a prevention repository. The system may further generate a causal link indication indicative of a causal link between the substance token and the condition token in response to the distance score being greater than a threshold value and detection of the absence of the preventative association between the substance token and the condition token.

Abstract: A system for determining adverse effects associated with a substance includes a side-effect recognizer, a relationship extractor, a processor, and a reporting system. The side-effect recognizer utilizes a first recurrent neural network (RNN) to identify a first portion of received data associated with an adverse effect to thereby determine the adverse effect associated with the received data. The relationship extractor utilizes a second RNN to identify a second portion of the received data associated with a substance and a third portion of the received data that indicates a relationship between the substance and the adverse effect to thereby determine the substance associated with the received data and the relationship between the substance and the adverse effect. The processor is in communication with the adverse effect recognizer and the substance relationship extractor and aggregates and relates the adverse effect, substance, and relationship.

Abstract: A device may receive multimedia data, metadata, and/or policy data. The device may process the policy data using a first set of techniques to determine a first set of embeddings for the policy data. The device may process the multimedia data or the metadata using a second set of techniques to determine a second set of embeddings for the multimedia data or the metadata. The device may process the first set of embeddings and the second set of embeddings using a knowledge graph to determine whether the multimedia content or the access by the user violates the policy. The device may perform an action based on a result of processing the first set of embeddings and the second set of embeddings. The action may relate to the multimedia content or the access to the multimedia content by the user.