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We present MedCATTrainer an interface for building, improving and customisinga given Named Entity Recognition and Linking (NER+L) model for biomedicaldomain text. NER+L is often used as a first step in deriving value fromclinical text. Collecting labelled data for training models is difficult due tothe need for specialist domain knowledge. MedCATTrainer offers an interactiveweb-interface to inspect and improve recognised entities from an underlyingNER+L model via active learning. Secondary use of data for clinical researchoften has task and context specific criteria. MedCATTrainer provides a furtherinterface to define and collect supervised learning training data forresearcher specific use cases. Initial results suggest our approach allows forefficient and accurate collection of research use case specific training data.

MedCATTrainer: A Biomedical Free Text Annotation Interface with Active Learning and Research Use Case Specific Customisation