Abstract: A mechanism is provided in a data processing system comprising a processor and a memory, the memory comprising instructions that are executed by the processor to specifically configure the processor to implement a repetitive portion identification and weighting engine. A machine learning model is trained for weighting repetitive portions of patient electronic medical records (EMRs). A repetitive portion identification component applies a plurality of templates to clinical notes of a patient EMR to identify one or more candidate portions that match at least one of the plurality of templates. A content analysis component performs content analysis on the one or more candidate portions to determine whether each given candidate portion is relevant. A weighting component assigns a relative weight to each given candidate portion based on relevance.

Abstract: A mechanism is provided in a data processing system comprising a processor and a memory, the memory comprising instructions that are executed by the processor to specifically configure the processor to implement a repetitive portion identification and weighting engine. A machine learning model is trained for weighting repetitive portions of patient electronic medical records (EMRs). A repetitive portion identification component applies a plurality of templates to clinical notes of a patient EMR to identify one or more candidate portions that match at least one of the plurality of templates. A content analysis component performs content analysis on the one or more candidate portions to determine whether each given candidate portion is relevant. A weighting component assigns a relative weight to each given candidate portion based on relevance.

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: Methods, devices, and systems (for outputting a case summary) receive an electronic medical record (EMR) (and generally electronic records) for the medical patient, extract medical data from the EMR, provide a list of medical problems relevant to the EMR, identifying relations between the medical problems and the medical data using a question-answering (QA) system, and output the clinical summary for the EMR. The clinical summary comprises the list of medical problems, the medical data, and the relations.

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: Automatic determination of underlying reasons for lack of treatment adherence is provided. In various embodiments, patient information for a patient is retrieved. The patient information comprises a treatment plan. A failure of compliance with the treatment plan by the patient is determined from the patient information. The failure of compliance and the patient information are evaluated to determine one or more potential cause of the failure of compliance. The one or more potential cause is provided to a user for review.

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: Embodiments of the invention include methods, systems, and computer program products for generating a medical problem list. A non-limiting example of the method includes receiving, by a processor, a plurality of disease categories. A disease category set that includes a plurality of top level disease categories is defined using the processor, wherein the disease category set is based at least in part upon the plurality of disease categories. The processor is used to extract a plurality of candidate training problems from an electronic patient record training set. The processor is used to assign each of the candidate training problems to the plurality of top level disease categories. The processor is used to generate a disease category model for each of the top level disease categories from the electronic patient record training set using a machine learning technique.

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: The present invention embodiments are directed to methods, systems, and computer programs for identifying relations, within at least one taxonomy, between taxonomy categories and concepts extracted from electronic content. The relations represent semantic similarities for the concepts. The concepts are clustered based on the identified relations within the at least one taxonomy.

Abstract: The present invention embodiments are directed to methods, systems, and computer programs for identifying relations, within at least one taxonomy, between taxonomy categories and concepts extracted from electronic content. The relations represent semantic similarities for the concepts. The concepts are clustered based on the identified relations within the at least one taxonomy.

Abstract: A mechanism is provided in a data processing system comprising a processor and a memory, the memory comprising instructions that are executed by the processor to specifically configure the processor to implement a repetitive portion identification and weighting engine. A machine learning model is trained for weighting repetitive portions of patient electronic medical records (EMRs). A repetitive portion identification component applies a plurality of templates to clinical notes of a patient EMR to identify one or more candidate portions that match at least one of the plurality of templates. A content analysis component performs content analysis on the one or more candidate portions to determine whether each given candidate portion is relevant. A weighting component assigns a relative weight to each given candidate portion based on relevance.

Abstract: A mechanism is provided for implementing an augmented reality display via a head mounted display (HMD) system that indicates areas of a patient's body corresponding to a medical condition and/or treatment of the patient overlayed on the actual view of the patient. A real-time image of an area of a patient's body being viewed by a medical professional is captured via the HMD system. One or more body parts of the patient are identified within the real-time image. The one or more identified body parks are correlated with the patient's electronic medical records (EMRs) indicating the medical condition and/or treatments associated with the patient. An augmented reality display is then generated in the HMD system of one or more areas of the patient's body corresponding to the medical condition and/or treatment of the patient overlaying the real-time image of the area of the patient's body.

Abstract: A mechanism is provided in a data processing system comprising a processor and a memory, the memory comprising instructions that are executed by the processor to specifically configure the processor to implement a repetitive portion identification and weighting engine. A machine learning model is trained for weighting repetitive portions of patient electronic medical records (EMRs). A repetitive portion identification component applies a plurality of templates to clinical notes of a patient EMR to identify one or more candidate portions that match at least one of the plurality of templates. A content analysis component performs content analysis on the one or more candidate portions to determine whether each given candidate portion is relevant. A weighting component assigns a relative weight to each given candidate portion based on relevance.

Abstract: A mechanism is provided for implementing an augmented reality display via a head mounted display (HMD) system that indicates areas of a patient's body corresponding to a medical condition and/or treatment of the patient overlayed on the actual view of the patient. A real-time image of an area of a patient's body being viewed by a medical professional is captured via the HMD system. One or more body parts of the patient are identified within the real-time image. The one or more identified body parts are correlated with the patient's electronic medical records (EMRs) indicating the medical condition and/or treatments associated with the patient. An augmented reality display is then generated in the HMD system of one or more areas of the patient's body corresponding to the medical condition and/or treatment of the patient overlaying the real-time image of the area of the patient's body.

Abstract: Methods, devices, and systems (for outputting a case summary) receive an electronic medical record (EMR) (and generally electronic records) for the medical patient, extract medical data from the EMR, provide a list of medical problems relevant to the EMR, identifying relations between the medical problems and the medical data using a question-answering (QA) system, and output the clinical summary for the EMR. The clinical summary comprises the list of medical problems, the medical data, and the relations.

Abstract: Automatic determination of underlying reasons for lack of treatment adherence is provided. In various embodiments, patient information for a patient is retrieved. The patient information comprises a treatment plan. A failure of compliance with the treatment plan by the patient is determined from the patient information. The failure of compliance and the patient information are evaluated to determine one or more potential cause of the failure of compliance. The one or more potential cause is provided to a user for review.

Abstract: Methods, devices, and systems (for outputting a case summary) receive an electronic medical record (EMR) for the medical patient, extract medical data from the EMR, provide a list of medical problems relevant to the EMR, identifying relations between the medical problems and the medical data using a question-answering (QA) system, and output the clinical summary for the EMR. The clinical summary comprises the list of medical problems, the medical data, and the relations.