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.
Build based on Healthcare NLP 6.4.0 version - https://nlp.johnsnowlabs.com/docs/en/jsl/jsl-release-notes#section-2
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 for the compute instance that runs this PDF de-identification pipeline. Pricing splits across two factors. First, the instance type: ml.m4.2xlarge, ml.m4.4xlarge, ml.c5.4xlarge, and ml.c5.9xlarge differ in CPU and memory size. Second, the inference mode: batch processing of grouped files or real-time processing of individual requests. Each instance-and-mode pairing is billed separately per host hour. Larger instances handle heavier workloads. You choose the combination that fits your volume, then pay only for the hours you run.
Top-of-mind questions for buyers
One host hour is one running hour of the compute instance that hosts the pipeline. Charges accrue only while the instance runs. A stopped or terminated instance stops software metering. Underlying AWS storage or related resources may still bill separately per AWS rules.
Batch mode processes grouped PDF files together, which fits scheduled or bulk de-identification runs. Real-time mode handles individual requests as they arrive, which fits on-demand processing through an API endpoint. Each mode bills per host hour on the instance you choose, so match the mode to your workflow.
No. The pipeline runs in-place inside your own cloud account, datacenter, or air-gapped network. Processing happens in memory, with zero data retention by default. Your billing covers the instance hours you run; it does not add per-token or per-document movement fees.
www.johnsnowlabs.com
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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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