Sold by: John Snow Labs
Deployed on AWS
Free Trial
Mask or Obfuscate Personal Health Information (PHI) in Spanish clinical notes.
Overview
The Clinical De-Identification model is designed to recognize and anonymize PHI in Spanish-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 4M chars per hour in real-time and 7M 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.
- Covered entities: LOCATION, MEDICALRECORD, ORGANIZATION, DOCTOR, PROFESSION, USERNAME, DATE, ZIP, PATIENT, PHONE, HOSPITAL, E-MAIL, AGE, SSN, SEX
- 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.
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.m5.2xlarge Inference (Batch) Recommended | Model inference on the ml.m5.2xlarge instance type, batch mode | $47.52 |
ml.m5.xlarge Inference (Real-Time) Recommended | Model inference on the ml.m5.xlarge instance type, real-time mode | $23.76 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $47.52 |
ml.m4.xlarge Inference (Real-Time) | Model inference on the ml.m4.xlarge instance type, real-time mode | $23.76 |
ml.m6i.2xlarge Inference (Batch) | Model inference on the ml.m6i.2xlarge instance type, batch mode | $47.52 |
ml.m7i.2xlarge Inference (Batch) | Model inference on the ml.m7i.2xlarge instance type, batch mode | $47.52 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $47.52 |
ml.c6i.2xlarge Inference (Batch) | Model inference on the ml.c6i.2xlarge instance type, batch mode | $47.52 |
ml.c7i.2xlarge Inference (Batch) | Model inference on the ml.c7i.2xlarge instance type, batch mode | $47.52 |
ml.r6i.2xlarge Inference (Batch) | Model inference on the ml.r6i.2xlarge instance type, batch mode | $47.52 |
Dimensions summary
You pay by the hour for the AWS instance that runs this de-identification model. Pricing splits into two processing modes. Batch mode runs on 2xlarge instances for high-volume jobs. Real-time mode runs on xlarge instances for on-demand requests. Within each mode, you choose from several instance families (m, c, and r series) across multiple generations. Compute-focused (c), general-purpose (m), and memory-focused (r) options let you match hardware to your workload. Each hour billed reflects the instance size and family you select, so cost scales with the compute capacity you choose.
Top-of-mind questions for buyers
One host hour covers the running time of the AWS instance hosting the model. You pay per hour the instance runs. Stopped instances stop accruing software charges. You may still owe underlying AWS storage fees for stopped instances, but the software meters running time only.
Batch mode runs on 2xlarge instances and processes documents in scheduled bulk jobs. Real-time mode runs on xlarge instances and handles requests as they arrive. Both bill by the hour the instance runs. Choose batch for high-volume jobs and real-time for on-demand processing.
Cost scales with the instance size and family you select. Compute-focused (c), general-purpose (m), and memory-focused (r) options each carry their own hourly rate. Newer generations and larger instances run at different hourly rates. Match the hardware to your workload to control 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.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
New Version for JSL libs 6.4.0
Additional details
Inputs
- Summary
Input Format
JSON Format
- Array of Text Documents: Use an array containing multiple text documents. Each element represents a separate text document.
{ "text": [ "Text document 1", "Text document 2", ... ] }
- Single Text Document: Provide a single text document as a string.
{ "text": "Single text document" }
JSON Lines (JSONL) Format
Provide input in JSON Lines format, where each line is a JSON object representing a text document.
{"text": "Text document 1"} {"text": "Text document 2"}
Important Parameter:
masking_policy: str
Users can select a masking policy to determine how sensitive entities are handled:
Example: "Nombre: Danilo. Apellidos: Ramos Portillo. NHC: 4376372. NASS: 35 61051794 56. Domicilio: Calle Fernando Higueras, 27, 7B."
masked: Default policy that masks entities with their type.
-> 'Nombre: . Apellidos: . NHC: . NASS: . Domicilio: , , 7B.'
obfuscated: Replaces sensitive entities with random values of the same type.
-> 'Nombre: Aurora Garrido Paez.Apellidos: Antonio González Cuevas.NHC: BBBBBBBBQR648597.NASS: 48.124.111S. Domicilio: Aurora Garrido Paez, 36, 7B.'
masked_fixed_length_chars: Masks entities with a fixed length of asterisks (*).
-> 'Nombre: ****. Apellidos: ****. NHC: ****. NASS: ****. Domicilio: ****, ****, 7B.'
masked_with_chars: Masks entities with asterisks (*).
-> 'Nombre: []. Apellidos: [**********]. NHC: []. NASS: []. Domicilio: [************], **, 7B.'
You can specify these parameters in the input as follows:
{ "text": [ "Text document 1", "Text document 2", ... ], "masking_policy": "masked",}
- Input MIME type
- application/json, application/jsonlines
https://github.com/JohnSnowLabs/spark-nlp-workshop/tree/master/products/sagemaker/models/es.deid.clinical/inputs/real-time
https://github.com/JohnSnowLabs/spark-nlp-workshop/tree/master/products/sagemaker/models/es.deid.clinical/inputs/batch
Resources
Vendor resources
Support
Vendor support
For any assistance, please reach out to support@johnsnowlabs.com .
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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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