The Clinical De-Identification model is designed to recognize and anonymize PHI in Romanian-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 7M chars per hour in real-time and 16M chars per hour in batch mode.
**Key Features:**
- The model is tuned to identify wide range of PHI elements in medical texts, ensuring comprehensive de-identification.
- The process aligns with GDPR 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 without compromising patient privacy.
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 the highest standards of data privacy and security.
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
You pay by the hour based on the compute instance you run, with no upfront commitment. Pricing splits into two processing modes. Batch mode uses 2xlarge instances across several instance families for bulk document processing. Real-time mode uses xlarge instances across the same families for on-demand requests. Within each mode, you choose from general-purpose (m-series), compute-optimized (c-series), and memory-optimized (r-series) families spanning older and newer generations. Your hourly rate scales with the instance you select. This runs the Romanian clinical de-identification model, which masks or obfuscates protected health information in medical text.
Top-of-mind questions for buyers
What does one HostHrs unit cover, and am I charged when the instance is idle or stopped?
One HostHrs unit is one hour of running the selected compute instance. Charges accrue per hour while the instance runs. A stopped or terminated instance stops software charges. Underlying AWS infrastructure fees may still apply for attached storage even when compute is paused.
What is the difference between batch mode and real-time mode for billing?
Both meter per instance-hour. Batch mode runs on 2xlarge instances and processes documents in bulk jobs. Real-time mode runs on xlarge instances and handles on-demand requests as they arrive. You pick the mode that matches your workload; each meters only the hours its instance runs.
Do the different instance families change what the model does, or only the cost?
The model output stays the same across all instances. It masks or obfuscates protected health information in Romanian medical text. Instance families differ in compute profile: m-series is general-purpose, c-series is compute-optimized, r-series is memory-optimized. Your choice affects processing speed and hourly rate, not the de-identification result.
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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:
Real-time inference
Deploy the model as an API endpoint for your applications. When you send data to the endpoint, SageMaker processes it and returns results by API response. The endpoint runs continuously until you delete it. You're billed for software and SageMaker infrastructure costs while the endpoint runs. AWS Marketplace models don't support Amazon SageMaker Asynchronous Inference. For more information, see Deploy models for real-time inference .
Batch transform
Deploy the model to process batches of data stored in Amazon Simple Storage Service (Amazon S3). SageMaker runs the job, processes your data, and returns results to Amazon S3. When complete, SageMaker stops the model. You're billed for software and SageMaker infrastructure costs only during the batch job. Duration depends on your model, instance type, and dataset size. AWS Marketplace models don't support Amazon SageMaker Asynchronous Inference. For more information, see Batch transform for inference with Amazon SageMaker AI .
Version release notes
Upgraded to johnsnowlabs Libs - 6.4.0
Additional details
Inputs
Outputs
Usage instructions
Sample notebooks
Inputs
Summary
Input Format
To use the model, you need to provide input in one of the following supported formats:
JSON Format
Provide input as JSON. We support two variations within this format:
Array of Text Documents: Use an array containing multiple text documents. Each element represents a separate text document.
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.
The PDF Obfuscation Pipeline is a powerful solution for transforming sensitive PDF documents into safe, shareable assets. It enables organizations to unlock the value of clinical data while ensuring strict compliance with HIPAA, GDPR, and institutional privacy standards.
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