# Comprehend Medical: a Named Entity Recognition and Relationship Extraction Web Service

arXiv: https://arxiv.org/abs/1910.07419 · HTML: https://mlsanity.com/paper/1910.07419/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Oct 2019.

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

- [Neural Natural Language Processing for Unstructured Data in Electronic Health Records: a Review](https://mlsanity.com/paper/2107.02975/) (2021)
- [Biomedical Data-to-Text Generation via Fine-Tuning Transformers](https://mlsanity.com/paper/2109.01518/) (2021)
- [Text Classification of Cancer Clinical Trial Eligibility Criteria](https://mlsanity.com/paper/2309.07812/) (2023)
- [Performant ASR Models for Medical Entities in Accented Speech](https://mlsanity.com/paper/2406.12387/) (2024)
- [Comprehensive Overview of Named Entity Recognition: Models, Domain-Specific Applications and Challenges](https://mlsanity.com/paper/2309.14084/) (2023)
- [Private Text Generation by Seeding Large Language Model Prompts](https://mlsanity.com/paper/2502.13193/) (2025)
- [Neural Entity Recognition with Gazetteer based Fusion](https://mlsanity.com/paper/2105.13225/) (2021)
- [DataWords: Getting Contrarian with Text, Structured Data and Explanations](https://mlsanity.com/paper/2111.05384/) (2021)
- [Controlled Randomness Improves the Performance of Transformer Models](https://mlsanity.com/paper/2310.13526/) (2023)
- [Weakly Supervised Medical Entity Extraction and Linking for Chief Complaints](https://mlsanity.com/paper/2509.01899/) (2025)

## Abstract (arXiv)

Comprehend Medical is a stateless and Health Insurance Portability and Accountability Act (HIPAA) eligible Named Entity Recognition (NER) and Relationship Extraction (RE) service launched under Amazon Web Services (AWS) trained using state-of-the-art deep learning models. Contrary to many existing open source tools, Comprehend Medical is scalable and does not require steep learning curve, dependencies, pipeline configurations, or installations. Currently, Comprehend Medical performs NER in five medical categories: Anatomy, Medical Condition, Medications, Protected Health Information (PHI) and Treatment, Test and Procedure (TTP). Additionally, the service provides relationship extraction for the detected entities as well as contextual information such as negation and temporality in the form of traits. Comprehend Medical provides two Application Programming Interfaces (API): 1) the NERe API which returns all the extracted named entities, their traits and the relationships between them and 2) the PHId API which returns just the protected health information contained in the text. Furthermore, Comprehend Medical is accessible through AWS Console, Java and Python Software Development Kit (SDK), making it easier for non-developers and developers to use.