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 mechanism is provided for implementing an anticipatory analytics processing mechanism for processing electronic medical records (EMRs) of a set of patients using anticipatory analytics to provide patient insights. Responsive to receiving a set of EMRs for a set of patients, each EMR in the set of EMRs is queued into one of a plurality of queues based on a set of work types. Responsive to receiving a request for work by a processing service indicating a work type to be provided, an EMR is identified from the plurality of queues based on the work type to be provided and a predetermined order of work. The identified EMR is dispatched to the processing service. Responsive to receiving a completion indication from the processing service, the EMR is either advanced to a queue associated with a next work type category or indicated that patient insight for a patient associated with the EMR is complete.

Abstract: A method of managing an electronic medical record comprises receiving patient data associated with the medical record. First encounter information in the patient data is identified and compared with second encounter information in a first encounter bundle in the medical record. Based on the comparing, the first encounter information and second encounter information are determined to be associated with the same encounter. The patient data is added to the first encounter bundle based on the determining. The first encounter bundle is analyzed, resulting in a computer-readable artifact for the first encounter bundle. A summary of the first encounter bundle is updated based in part on the computer-readable artifact.