MIMICause: Representation and automatic extraction of causal relation types from clinical notes
Vivek Khetan, Md Imbesat Rizvi, Jessica E. Huber, Paige Bartusiak, Bogdan Sacaleanu, Andrew Fano
2022Findings of the Association for Computational Linguistics: ACL 202218 citationsDOIOpen Access PDF
Abstract
Understanding causal narratives communicated in clinical notes can help make strides towards personalized healthcare. Extracted causal information from clinical notes can be combined with structured EHR data such as patients' demographics, diagnoses, and medications. This will enhance healthcare providers' ability to identify aspects of a patient's story communicated in the clinical notes and help make more informed decisions.
Topics & Concepts
Computer scienceNatural language processingAnnotationSentenceBaseline (sea)Relationship extractionNarrativeTask (project management)Artificial intelligenceRepresentation (politics)F1 scoreQuality (philosophy)Relation (database)Information retrievalData scienceInformation extractionLinguisticsData miningOceanographyPolitical sciencePoliticsEpistemologyManagementPhilosophyEconomicsLawGeologyBiomedical Text Mining and OntologiesTopic ModelingNatural Language Processing Techniques