Sold by: OpenMed
Deployed on AWS
Open-source NER model for disease entities in biomedical and clinical text. Trained on NCBI-Disease and optimized for state-of-the-art precision, it enables reliable extraction with fast, easy deployment via Hugging Face Transformers.
Overview
Open-Source Disease NER Model: State-of-the-Art Biomedical Entity Recognition Discover a powerful, open-source Named Entity Recognition (NER) model specially fine-tuned for accurate identification and extraction of disease entities from biomedical and clinical documents. Engineered on the curated NCBI-Disease dataset, this model surpasses licensed alternatives with industry-leading precision. Why Choose OpenMed NCBI Disease NER Model? Open-Source & Free Forever: No licensing fees fully accessible to empower your biomedical research. State-of-the-Art Accuracy: Achieve unmatched precision in extracting disease names, outperforming commercial solutions. Clinical & Biomedical Excellence: Expertly validated on clinical benchmarks for reliability in disease detection, healthcare analytics, and clinical studies. Easy & Fast Integration: Seamlessly integrates into the Hugging Face Transformers ecosystem for effortless deployment. Ideal for Biomedical Applications Including: Disease detection and monitoring Disease extraction from patient records Adverse event monitoring Literature mining for disease research Biomedical knowledge graph construction Clinical informatics and pathology research Built on the NCBI-Disease Dataset: This specialized dataset contains comprehensive annotations for disease names and conditions, making it ideal for clinical informatics, pathology research, and advanced biomedical text mining. Entity Types Supported: B-Disease OpenMed-Disease Experience industry-leading biomedical NER performance open-source and completely free.
Highlights
- Open-Source and Free Forever: Eliminate licensing costs while accessing state-of-the-art biomedical entity recognition.
- Clinical-Grade Accuracy: Superior precision validated on the NCBI-Disease dataset, ideal for clinical analytics and decision support.
- Easy Integration: Fully compatible with Hugging Face Transformers for fast and effortless deployment.
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Dimensions summary
This model is free to use; you pay only for the AWS compute instance you run it on. You choose between two deployment modes. Batch mode processes data in bulk and runs on four instance types. Real-time mode serves live requests and runs on five instance types. Within each mode, you pick a machine size. Larger sizes give more compute per hour. All dimensions bill per host hour, so cost scales with how long the instance runs. Pick the mode and size that match your workload.
Top-of-mind questions for buyers
One host hour is one hour that your chosen AWS instance runs the model. The software itself is free, so charges come from the AWS compute time. If the instance is stopped, it stops accruing host hours. Underlying AWS storage fees may still apply while resources persist.
Batch mode processes data in bulk and runs on four instance types. Real-time mode serves live requests and runs on five instance types. Both bill per host hour. Batch suits scheduled bulk jobs you run and stop. Real-time suits endpoints kept running to answer requests as they arrive.
The model performs clinical named-entity recognition, identifying medical entities in text. The model license is free, so you pay only for the AWS instance host hours you run. There is no separate software charge added to the compute cost.
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Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
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Version release notes
Initial release of OpenMed NER models for comprehensive biomedical entity recognition. These models deliver high‑precision token classification across clinical and research text, covering organisms and species, chemicals, diseases and phenotypes, genes and proteins, genetic variants/genomics, anatomy, oncology (including CLL), and related biomedical concepts. Designed for enterprise‑grade accuracy, optimized performance on medical text, and production‑ready reliability for healthcare and life sciences applications.
Additional details
Inputs
- Summary
This model accepts clinical text, research papers, and biomedical documents as input. The input should be provided as JSON with an "inputs" field containing the text to analyze. The model processes natural language text and identifies medical entities including organisms, species, diseases, and other biomedical terms. Input text can range from short clinical notes to longer research documents, with optimal performance on medical and scientific content.
- Input MIME type
- application/json
{"inputs": "Patient presents with type 2 diabetes mellitus and hypertension. History notable for chronic kidney disease."}
{"inputs": ["Patient presents with type 2 diabetes mellitus and hypertension. History notable for chronic kidney disease.", "CT confirms pneumonia with pleural effusion. Prior records indicate myocardial infarction and heart failure."]}
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For any questions or support, please join the discussions on OpenMed:
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