Abstract: Systems, devices, computer-implemented methods, and/or computer program products that can facilitate event extraction from heterogeneous data sources using an active learning framework are provided. In one example, a system can comprise a processor that executes computer executable components stored in memory. The computer executable components can comprise a training component and a learning-based sample component. The training component can train one or more models to recognize one or more events from data input into the system, extract one or more triggers from the data input into the system, extract one or more arguments from the data input into the system, or extract one or more roles from the data input into the system. The learning-based sample component can determine a sampled dataset by applying a learning-based sample to the one or more models.

Abstract: 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.

Abstract: A clinical information extraction and training mechanism is provided for automatically extracting and identifying information in plain text narratives in a set of electronic medical records. The mechanism segments each clinical note in a plurality of clinical notes into one or more identified sections, labels each identified section with an associated tag, and generate a tag data structure utilizing explicitly tagged sequences of sentences and associated tags. The mechanism performs statistical analysis of the identified sections that contain tags identified in the tag data structure to identify one or more valid stop/start conditions; extracts a first set of positive examples of sentences for a selected type of information, and then trains a cognitive system to identify sentences in the plurality of clinical notes that fail to have a tag associated with the selected type using the positive examples of sentences for different types of information.

Abstract: A service receives a persuasion-based input comprising a text and one or more marketing objectives to persuade a desired response. The service evaluates persuasion values of text segments of the text and persuasion transition values consecutively between respective persuasion values of the persuasion values across the text segments. The service generates a desired curve of persuasion factors across the text segments according to the one or more marketing objectives. The service recommends one or more replacement words to replace one or more selected words in text to move a deviation between the persuasion values and transition values in comparison to the desired curve of persuasion factors.

Abstract: Text data including at least named entities can be received. From the named entities, continuous entities, overlapping entities and disjoint entities can be identified. The overlapping entities can be transformed into continuous entities. The continuous entities, the transformed entities and the disjoint entities can be encoded. The encoded entities can be input to a machine learning language model to train the machine learning model to predict candidate entities. The predicted entities can be decoded to reconstruct the predicted entities.

Abstract: Text data including at least named entities can be received. From the named entities, continuous entities, overlapping entities and disjoint entities can be identified. The overlapping entities can be transformed into continuous entities. The continuous entities, the transformed entities and the disjoint entities can be encoded. The encoded entities can be input to a machine learning language model to train the machine learning model to predict candidate entities. The predicted entities can be decoded to reconstruct the predicted entities.

Abstract: A service receives a persuasion-based input comprising a text and one or more marketing objectives to persuade a desired response. The service evaluates persuasion values of text segments of the text and persuasion transition values consecutively between respective persuasion values of the persuasion values across the text segments. The service generates a desired curve of persuasion factors across the text segments according to the one or more marketing objectives. The service recommends one or more replacement words to replace one or more selected words in text to move a deviation between the persuasion values and transition values in comparison to the desired curve of persuasion factors.

Abstract: A clinical information extraction and training mechanism is provided for automatically extracting and identifying information in plain text narratives in a set of electronic medical records. The mechanism segments each clinical note in a plurality of clinical notes into one or more identified sections, labels each identified section with an associated tag, and generate a tag data structure utilizing explicitly tagged sequences of sentences and associated tags. The mechanism performs statistical analysis of the identified sections that contain tags identified in the tag data structure to identify one or more valid stop/start conditions; extracts a first set of positive examples of sentences for a selected type of information, and then trains a cognitive system to identify sentences in the plurality of clinical notes that fail to have a tag associated with the selected type using the positive examples of sentences for different types of information.

Abstract: Mechanisms are provided for presentation of pertinent information for a medical treatment plan. The mechanisms receive a medical condition for an interaction with a patient, and a medical mental model that emulates thinking of a medical professional with regard to reviewing a patient electronic medical record (EMR) to identify pertinent information for a medical treatment plan to treat the medical condition based on categorizations of elements in the patient EMR. The mechanisms analyze the EMR for the patient to identify a portion of the EMR relevant to the medical treatment plan using the categorizations, and analyze the identified portion of the EMR to extract relevant patient information that is directed to the medical treatment plan for the medical condition based on the categorizations. The mechanisms generate and output a cognitive summary correlating the extracted relevant patient information and the medical treatment plan in association with the medical condition.