Methods and systems for artificial intelligence (AI)-assisted document annotation and training of machine learning-based models for document data extraction are described. The methods and systems described herein take advantage of a continuous machine learning approach to create document processing pipelines that provide accurate and efficient data extraction from documents that include structured text, semi-structured text, unstructured text, or any combination thereof.

Systems, devices, computer-implemented methods, and/or computer program products that facilitate artificial intelligence (AI)-assisted curation of non-pharmaceutical intervention (NPI) data from heterogeneous data sources. In one example, a system can comprise a processor that executes computer executable components stored in memory. The computer executable components can comprise an extraction component and a change detection component. The extraction component can extract candidate non-pharmaceutical intervention (NPI) events from data associated with a defined disease. The change detection component can evaluate the candidate NPI events for inclusion in a dataset storing NPI events in a defined format.

Listeners to media programs provide feedback to creators or other entities associated with the media programs in the form of one or more spoken utterances. When a listener to a media program speaks one or more words to a microphone or other system, the words are captured and processed to determine an emotion of the listener, or to determine whether the words include any objectionable content. Data including the spoken words is captured and stored, and presented to the creator of the media program. Notifications of the utterances are provided to the creator, who may identify one of the utterances, and include the utterance in the media program, or invite the listener who provided the utterances to participate in the media program.

Provided is a method including obtaining a first data object including a first set of data entries, wherein each data entry of the first set of data entries includes text content associated with a time entry. The method includes generating a first data object score using the text content and the time entries included in the first set of data entries and using scoring parameters, determine that the first data object score satisfies a data object score condition; perform in response to the first data object score satisfying the data object score condition, a condition-specific action associated with the data object score condition.

Methods, apparatuses, and computer program products are described herein that are configured to express a time in an output text. In some example embodiments, a method is provided that comprises identifying a time period to be described linguistically in an output text. The method of this embodiment may also include identifying a communicative context for the output text. The method of this embodiment may also include determining one or more temporal reference frames that are applicable to the time period and a domain defined by the communicative context. The method of this embodiment may also include generating a phrase specification that linguistically describes the time period based on the descriptor that is defined by a temporal reference frame of the one or more temporal reference frames. In some examples, the descriptor specifies a time window that is inclusive of at least a portion of the time period to be described linguistically.

Methods, apparatuses, and computer program products are described herein that are configured to express a time in an output text. In some example embodiments, a method is provided that comprises identifying a time period to be described linguistically in an output text. The method of this embodiment may also include identifying a communicative context for the output text. The method of this embodiment may also include determining one or more temporal reference frames that are applicable to the time period and a domain defined by the communicative context. The method of this embodiment may also include generating a phrase specification that linguistically describes the time period based on the descriptor that is defined by a temporal reference frame of the one or more temporal reference frames. In some examples, the descriptor specifies a time window that is inclusive of at least a portion of the time period to be described linguistically.

In clinical documentation, mere documentation of a condition in a patient's records may not be enough. To be considered sufficiently documented, the patient's record needs to show that no documentation drop-offs (DDOs) have occurred over the course of the patient's stay. However, DDOs can be extremely difficult to detect. To solve this problem, the invention trains time-sensitive deep learning (DL) models on a per condition basis using actual and/or synthetic patient data. Utilizing an ontology, grouped concepts can be generated on the fly from real-time hospital data and used to generate time-series data that can then be analyzed by trained time-sensitive DL models to determine whether a DDO for a condition has occurred during the stay. Non-time-sensitive models can be used to detect all the conditions documented during the stay. Outcomes from the models can be compared to determine whether to notify a user that a DDO has occurred.

A natural language model can be primed utilizing optimized examples generated from a labeled knowledge graph corresponding to an independently developed application program. Parsing of the labeled knowledge graph can include the identification of triples, comprising a source node, a destination node, and a link between them, each of which can be labeled. One or more natural language input examples can be generated from an individual triple by concatenating the natural language words or phrases utilized to label the source node in the link. Determinations that subsequently received natural language user input is similar to the generated examples can result in an identification of the triple, which can, in turn, trigger the performance of a function associated with the destination node of the triple. Labels can include preferred labels and alternative labels, and various permutations thereof can be concatenated to generate alternative natural language input examples.

A natural language model can be primed utilizing optimized examples generated from a labeled knowledge graph corresponding to an independently developed application program. Parsing of the labeled knowledge graph can include the identification of triples, comprising a source node, a destination node, and a link between them, each of which can be labeled. One or more natural language input examples can be generated from an individual triple by concatenating the natural language words or phrases utilized to label the source node in the link. Determinations that subsequently received natural language user input is similar to the generated examples can result in an identification of the triple, which can, in turn, trigger the performance of a function associated with the destination node of the triple. Labels can include preferred labels and alternative labels, and various permutations thereof can be concatenated to generate alternative natural language input examples.

A service identifies a level of specificity of one or more identified entities in a user input comprising a query, within one of multiple levels of a hierarchy of a hierarchical coding system. Responsive to determining that additional levels of specificity beyond the identified level of specificity are recommended to return a minimum answer set to the query, the service returns one or more answers requesting one or more additional inputs refining the query based on one or more values identified in a next level. Responsive to determining that no additional levels of specificity beyond the identified level of specificity are recommended to return the minimum answer set to the query, the service returns an answer set comprising a selection of information for the current level of specificity from an ingested corpus of knowledge mapped to the hierarchical coding system.