Sold by: John Snow Labs
Deployed on AWS
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Mask or Obfuscate Personal Health Information (PHI) in English clinical notes.
Overview
The Clinical De-Identification model is designed to recognize and anonymize PHI in English-language clinical notes. It employs state-of-the-art natural language processing techniques to detect sensitive information such as patient names, addresses, medical record numbers, and other identifiers. Once identified, the PHI is effectively masked or obfuscated, rendering the text safe for broader use while maintaining its informational integrity.
IMPORTANT USAGE INFORMATION:
After subscribing to this product and creating a SageMaker endpoint, billing occurs on an HOURLY BASIS for as long as the endpoint is running.
-Charges apply even if the endpoint is idle and not actively processing requests.
-To stop charges, you MUST DELETE the endpoint in your SageMaker console.
-Simply stopping requests will NOT stop billing.
This ensures you are only billed for the time you actively use the service.
Highlights
- Process up to **25 M chars per hour** in real-time, **65 M chars per hour** in batch mode. **Key Features:** - Model finely tuned to identify a wide range of PHI elements in medical texts, ensuring comprehensive de-identification. - The de-identification process aligns with HIPAA and other healthcare privacy regulations, aiding in legal compliance and data protection. - Ideal for research, analytics, and training purposes, this model enables the safe utilization of medical texts.
- Covered entities: AGE, CITY, COUNTRY, DATE, DOCTOR, EMAIL, HOSPITAL, IDNUM, ORGANIZATION, PATIENT, PHONE, PROFESSION, STATE, STREET, USERNAME, ZIP. including handwritten signatures and other identifiers.
- This model is a useful asset in the healthcare and research sectors, where the protection of patient privacy is paramount. It allows for the ethical and legal use of valuable medical data, promoting research and analysis while upholding
Details
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Pricing
Free trial
Try this product free for 15 days according to the free trial terms set by the vendor.
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.m4.4xlarge Inference (Batch) Recommended | Model inference on the ml.m4.4xlarge instance type, batch mode | $95.04 |
ml.m4.4xlarge Inference (Real-Time) Recommended | Model inference on the ml.m4.4xlarge instance type, real-time mode | $95.04 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $95.04 |
ml.c5.4xlarge Inference (Batch) | Model inference on the ml.c5.4xlarge instance type, batch mode | $95.04 |
ml.c5.9xlarge Inference (Batch) | Model inference on the ml.c5.9xlarge instance type, batch mode | $95.04 |
ml.m4.2xlarge Inference (Real-Time) | Model inference on the ml.m4.2xlarge instance type, real-time mode | $95.04 |
ml.c5.4xlarge Inference (Real-Time) | Model inference on the ml.c5.4xlarge instance type, real-time mode | $95.04 |
ml.c5.9xlarge Inference (Real-Time) | Model inference on the ml.c5.9xlarge instance type, real-time mode | $95.04 |
Dimensions summary
You pay by the hour based on the AWS instance you run this PDF de-identification software on. Three instance types are available: ml.m4.4xlarge, ml.c5.4xlarge, and ml.c5.9xlarge. Larger instances carry higher hourly rates but offer more compute for heavier workloads. Each instance also comes in two processing modes. Batch mode handles groups of documents at once. Real-time mode processes documents on demand. Your total cost depends on which instance you choose, which mode you use, and how many hours you run it. There is no per-document or per-token charge.
Top-of-mind questions for buyers
One HostHrs unit equals one hour that your chosen AWS instance runs the software. You pay per running hour, not per document. When you stop the instance, software charges stop. Underlying AWS infrastructure fees may still apply separately depending on your storage and instance state.
Both modes bill per running hour on the same instance types. Batch mode processes groups of PDF documents together in scheduled runs. Real-time mode processes documents on demand as requests arrive. Your cost depends on how long the instance runs in either mode, not the number of documents.
Each instance type carries its own hourly rate. Moving from ml.c5.4xlarge to ml.c5.9xlarge or ml.m4.4xlarge changes your rate. Larger instances give more compute for heavier PDF de-identification workloads. The change is not automatic; you choose the instance when you deploy, and your bill reflects that choice.
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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.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
Updated underlying johnsnowlabs libraries to version 6.4.0
Additional details
Inputs
- Summary
Supported Formats
Format : PDF (single-page or multi-page)
Content-Type: application/octet-stream or application/pdf
Notes
- Input must be a valid PDF file sent as raw bytes.
- Both single-page and multi-page PDFs are supported.
- For batch transform jobs, upload each PDF as a separate S3 object and use SplitType: None with BatchStrategy: SingleRecord to prevent binary splitting.
- Input MIME type
- application/octet-stream, application/pdf
https://github.com/JohnSnowLabs/spark-nlp-workshop/tree/master/products/sagemaker/models/pdf_deid_multi_model_context_pipeline/inputs/real-time
https://github.com/JohnSnowLabs/spark-nlp-workshop/tree/master/products/sagemaker/models/pdf_deid_multi_model_context_pipeline/inputs/batch
Resources
Vendor resources
Support
Vendor support
For any assistance, please reach out to support@johnsnowlabs.com .
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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