The Clinical De-Identification model is designed to recognize and anonymize PHI in Arabic-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 8M chars per hour in real-time and 14M 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 for the compute instance running the model, based on the AWS SageMaker instance you select. Charges follow how long each instance runs (HostHrs). The options split into two processing modes. Batch mode uses 2xlarge instances for processing sets of records at once. Real-time mode uses xlarge instances for on-demand requests. Within each mode, you choose from several instance families that vary by compute, memory, and generation. Pick the mode and instance size that fit your workload; cost scales with instance choice and hours of use.
Top-of-mind questions for buyers
What am I paying for with the HostHrs unit on each instance?
You pay for each hour an AWS SageMaker instance runs the model. Billing counts the running time of the instance you choose, not the number of documents processed. An instance running one hour equals one HostHr, whether it handles one record or many.
How do batch mode and real-time mode differ for billing?
Batch mode runs on 2xlarge instances to process sets of records together, then stops. Real-time mode runs on xlarge instances that stay available for on-demand requests. Both meter by instance-hour. Batch fits scheduled bulk jobs; real-time fits continuous request-handling.
Am I charged when an instance is stopped or idle between jobs?
Charges accrue only while the instance runs and accumulates HostHrs. A stopped instance does not add software hours. To control cost in batch mode, run the instance for the job and stop it afterward. Real-time instances accrue hours the whole time they stay available.
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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