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 and methods for adverse reaction detection. An electronic input is received, the electronic input comprising a drug and outcome pair. The drug and outcome pair is classified as having one of a positive control and a negative control and a probability measure of an adverse drug event relation is determined based on results of the classification. A neural network is trained to jointly leverage reported drug, outcome information and a heterogenous semantic hierarchy, and the classification of a candidate drug is performed using the trained neural network.

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: 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: Systems, computer-implemented methods, and computer program products that can facilitate summarization of medication events based on multidimensional medication event data extracted from a data source are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise an extraction component that employs a model to extract multidimensional medication event data of one or more medication events from at least one data source. The computer executable components can further comprise a classification component that classifies the one or more medication events into orthogonal dimensions based on the multidimensional medication event data.

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: An approach is provided to receive, at a question answering (QA) system, a question and identify a politeness corresponding to a number of terms corresponding to the question that are included in a corpus of the QA system. The approach identifies the politeness of one or more terms included in each of a set of candidate answers responsive to the question. Finally, the approach scores each of the candidate answers, with the scoring being based, in part, on the politeness identified for each of the terms.

Abstract: An approach is provided for automatically predicting an event occurrence based on a question from an end user presented using a near-real-time natural language processing (NLP) analysis to generate, score and rank a plurality of event occurrences based on a plurality of question context parameters extracted from the question, one or more user profile parameters for the end user, and the one or more historical questions, answers, and events having a specified spatial and/or temporal proximity to the question which are identified by an information handling system. In the approach, performed by an information handling system, a top ranked event occurrence from the ranked plurality of event occurrences is selected for inclusion in a notification message that is communicated or broadcast to the end user, as well as other users engaged with the information handling system and/or first responders in the affected area.

Abstract: Natural language processing (NLP) with awareness of textual polarity. An NLP system, such as a search engine or a Question-Answering (QA) system receives input text for processing. The input text may be a text fragment, a search phrase, a question having a general type, or a polar question having a yes or no answer. The NLP system identifies textual polarity and provides responses to the input text (for example, in answer form) based on identifying evidence whose selection, scoring, and processing, is informed by the textual polarity of the input text, and the textual polarity of candidate evidence passages.

Abstract: Natural language processing (NLP) with awareness of textual polarity. An NLP system, such as a search engine or a Question-Answering (QA) system receives input text for processing. The input text may be a text fragment, a search phrase, a question having a general type, or a polar question having a yes or no answer. The NLP system identifies textual polarity and provides responses to the input text (for example, in answer form) based on identifying evidence whose selection, scoring, and processing, is informed by the textual polarity of the input text, and the textual polarity of candidate evidence passages.

Abstract: An approach is provided for automatically predicting an event occurrence based on a question from an end user presented using a near-real-time natural language processing (NLP) analysis to generate, score and rank a plurality of event occurrences based on a plurality of question context parameters extracted from the question, one or more user profile parameters for the end user, and the one or more historical questions, answers, and events having a specified spatial and/or temporal proximity to the question which are identified by an information handling system. In the approach, performed by an information handling system, a top ranked event occurrence from the ranked plurality of event occurrences is selected for inclusion in a notification message that is communicated or broadcast to the end user, as well as other users engaged with the information handling system and/or first responders in the affected area.

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.

Abstract: An approach is provided that receives a word that belongs to a first natural language and retrieves a first set of complexity data pertaining to the word in the first natural language. The approach translates the word to one or more translated words, with each of the translated words corresponding to one or more second natural languages. The approach then retrieves sets of complexity data, with the sets of complexity data corresponding to a different translated word. The approach determines a complexity of the word in the first natural language based on an analysis of the first and second sets of complexity data.

Abstract: An approach is provided that returns a simplified set of text to a user of a natural language processing (NLP) system with the simplified set of text having a complexity appropriate to the reading level of the user. The approach receives a word that belongs to a first natural language and retrieves a first set of complexity data pertaining to the word in the first natural language. The approach translates the word to one or more translated words, with each of the translated words corresponding to one or more second natural languages. The approach then retrieves sets of complexity data, with the sets of complexity data corresponding to a different translated word. The approach determines a complexity of the word in the first natural language based on an analysis of the first and second sets of complexity data.

Abstract: An approach is provided that receives a word that belongs to a first natural language and retrieves a first set of complexity data pertaining to the word in the first natural language. The approach translates the word to one or more translated words, with each of the translated words corresponding to one or more second natural languages. The approach then retrieves sets of complexity data, with the sets of complexity data corresponding to a different translated word. The approach determines a complexity of the word in the first natural language based on an analysis of the first and second sets of complexity data.

Abstract: An approach is provided that returns a simplified set of text to a user of a natural language processing (NLP) system with the simplified set of text having a complexity appropriate to the reading level of the user. The approach receives a word that belongs to a first natural language and retrieves a first set of complexity data pertaining to the word in the first natural language. The approach translates the word to one or more translated words, with each of the translated words corresponding to one or more second natural languages. The approach then retrieves sets of complexity data, with the sets of complexity data corresponding to a different translated word. The approach determines a complexity of the word in the first natural language based on an analysis of the first and second sets of complexity data.

Abstract: An approach is provided that receives a word that belongs to a first natural language and retrieves a first set of complexity data pertaining to the word in the first natural language. The approach translates the word to one or more translated words, with each of the translated words corresponding to one or more second natural languages. The approach then retrieves sets of complexity data, with the sets of complexity data corresponding to a different translated word. The approach determines a complexity of the word in the first natural language based on an analysis of the first and second sets of complexity data.

Abstract: An approach is provided that receives a word that belongs to a first natural language and retrieves a first set of complexity data pertaining to the word in the first natural language. The approach translates the word to one or more translated words, with each of the translated words corresponding to one or more second natural languages. The approach then retrieves sets of complexity data, with the sets of complexity data corresponding to a different translated word. The approach determines a complexity of the word in the first natural language based on an analysis of the first and second sets of complexity data.