  Electronic health records (EHR) contain large volumes of unstructured text,
requiring the application of Information Extraction (IE) technologies to enable
clinical analysis. We present the open-source Medical Concept Annotation
Toolkit (MedCAT) that provides: a) a novel self-supervised machine learning
algorithm for extracting concepts using any concept vocabulary including
UMLS/SNOMED-CT; b) a feature-rich annotation interface for customising and
training IE models; and c) integrations to the broader CogStack ecosystem for
vendor-agnostic health system deployment. We show improved performance in
extracting UMLS concepts from open datasets (F1:0.448-0.738 vs 0.429-0.650).
Further real-world validation demonstrates SNOMED-CT extraction at 3 large
London hospitals with self-supervised training over ~8.8B words from ~17M
clinical records and further fine-tuning with ~6K clinician annotated examples.
We show strong transferability (F1 > 0.94) between hospitals, datasets, and
concept types indicating cross-domain EHR-agnostic utility for accelerated
clinical and research use cases.

Computation and Language;Artificial Intelligence;Machine Learning