Comprehend Medical: a Named Entity Recognition and Relationship Extraction Web Service
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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.
Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). First posted on arXiv 15 Oct 2019.
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Citing papers per year: 2021: 4; 2022: 0; 2023: 3; 2024: 1; 2025: 2.
10 papers.
- 2021Neural Natural Language Processing for Unstructured Data in Electronic Health Records: a Reviewcited by 35
- 2021Biomedical Data-to-Text Generation via Fine-Tuning Transformerscited by 9
- 2023Text Classification of Cancer Clinical Trial Eligibility Criteriacited by 7
- 2024Performant ASR Models for Medical Entities in Accented Speechcited by 7
- 2023Comprehensive Overview of Named Entity Recognition: Models, Domain-Specific Applications and Challengescited by 6
- 2025Private Text Generation by Seeding Large Language Model Promptscited by 2
- 2021Neural Entity Recognition with Gazetteer based Fusioncited by 1
- 2021DataWords: Getting Contrarian with Text, Structured Data and Explanationscited by 0
- 2023Controlled Randomness Improves the Performance of Transformer Modelscited by 0
- 2025Weakly Supervised Medical Entity Extraction and Linking for Chief Complaintscited by 0
- 2025Weakly Supervised Medical Entity Extraction and Linking for Chief Complaints2 Sep 2025
- 2025Private Text Generation by Seeding Large Language Model Prompts18 Feb 2025
- 2024Performant ASR Models for Medical Entities in Accented Speech18 Jun 2024
- 2023Controlled Randomness Improves the Performance of Transformer Models20 Oct 2023
- 2023Comprehensive Overview of Named Entity Recognition: Models, Domain-Specific Applications and Challenges25 Sep 2023
- 2023Text Classification of Cancer Clinical Trial Eligibility Criteria14 Sep 2023
- 2021DataWords: Getting Contrarian with Text, Structured Data and Explanations9 Nov 2021
- 2021Biomedical Data-to-Text Generation via Fine-Tuning Transformers3 Sep 2021
- 2021Neural Natural Language Processing for Unstructured Data in Electronic Health Records: a Review7 Jul 2021
- 2021Neural Entity Recognition with Gazetteer based Fusion27 May 2021