Description This is a BERT-based model for classification of clinical documents sections. This model is trained on clinical document sections without the section header in the text, e.g., when splitting the document with ChunkSentenceSplitter annotator with parameter setInsertChunk=False. Predicted Entities Consultation and Referral, Habits, Complications and Risk Factors, Diagnostic and Laboratory Data, Discharge Information, History, Impression, Patient Information, Procedures, Other Live D...| Spark NLP
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Cross-listing allows you to move section enrollments from individual courses and combine them into one course. This feature is helpful for instructors who teach several sections of the same course and only want to manage course data in one location. Instructors can allow students to view users in ot...| Instructure Community