# MedCAT -- Medical Concept Annotation Tool

arXiv: https://arxiv.org/abs/1912.10166 · HTML: https://mlsanity.com/paper/1912.10166/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Dec 2019.

## Built on by (most cited first, 10 of 10)

- [MedJEx: A Medical Jargon Extraction Model with Wiki's Hyperlink Span and Contextualized Masked Language Model Score](https://mlsanity.com/paper/2210.05875/) (2022)
- [Estimating Redundancy in Clinical Text](https://mlsanity.com/paper/2105.11832/) (2021)
- [Self-Supervised Detection of Contextual Synonyms in a Multi-Class Setting: Phenotype Annotation Use Case](https://mlsanity.com/paper/2109.01935/) (2021)
- [A Hybrid Framework with Large Language Models for Rare Disease Phenotyping](https://mlsanity.com/paper/2405.10440/) (2024)
- [Clinical Utility of the Automatic Phenotype Annotation in Unstructured Clinical Notes: ICU Use Cases](https://mlsanity.com/paper/2107.11665/) (2021)
- [Unsupervised Numerical Reasoning to Extract Phenotypes from Clinical Text by Leveraging External Knowledge](https://mlsanity.com/paper/2204.10202/) (2022)
- [A Scalable Workflow to Build Machine Learning Classifiers with Clinician-in-the-Loop to Identify Patients in Specific Diseases](https://mlsanity.com/paper/2205.08891/) (2022)
- [Impact of translation on biomedical information extraction from real-life clinical notes](https://mlsanity.com/paper/2306.02042/) (2023)
- [MED-SE: Medical Entity Definition-based Sentence Embedding](https://mlsanity.com/paper/2212.04734/) (2022)
- [CUICurate: A GraphRAG-based Framework for Automated Clinical Concept Curation for NLP applications](https://mlsanity.com/paper/2602.17949/) (2026)

## Abstract (arXiv)

Biomedical documents such as Electronic Health Records (EHRs) contain a large amount of information in an unstructured format. The data in EHRs is a hugely valuable resource documenting clinical narratives and decisions, but whilst the text can be easily understood by human doctors it is challenging to use in research and clinical applications. To uncover the potential of biomedical documents we need to extract and structure the information they contain. The task at hand is Named Entity Recognition and Linking (NER+L). The number of entities, ambiguity of words, overlapping and nesting make the biomedical area significantly more difficult than many others. To overcome these difficulties, we have developed the Medical Concept Annotation Tool (MedCAT), an open-source unsupervised approach to NER+L. MedCAT uses unsupervised machine learning to disambiguate entities. It was validated on MIMIC-III (a freely accessible critical care database) and MedMentions (Biomedical papers annotated with mentions from the Unified Medical Language System). In case of NER+L, the comparison with existing tools shows that MedCAT improves the previous best with only unsupervised learning (F1=0.848 vs 0.691 for disease detection; F1=0.710 vs. 0.222 for general concept detection). A qualitative analysis of the vector embeddings learnt by MedCAT shows that it captures latent medical knowledge available in EHRs (MIMIC-III). Unsupervised learning can improve the performance of large scale entity extraction, but it has some limitations when working with only a couple of entities and a small dataset. In that case options are supervised learning or active learning, both of which are supported in MedCAT via the MedCATtrainer extension. Our approach can detect and link millions of different biomedical concepts with state-of-the-art performance, whilst being lightweight, fast and easy to use.