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