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 compute instance that runs the de-identification model. Pricing splits into two usage modes. Batch mode covers ten instance types, spanning the m4, m5, m6i, m7i, c5, c6i, c7i, r6i, and r7i families, all in the 2xlarge size. Real-time mode covers nine instance types across the m4, m5, m6i, m7i, c5, c6i, c7i, r5, r6i, and r7i families, all in the xlarge size. The rate varies by the instance family and size you choose. You control total cost by matching instance choice to your workload.
Top-of-mind questions for buyers
You pay for each hour a chosen SageMaker instance runs the model. One host-hour equals one running instance for one hour. Billing meters actual running time. Instance size and family set the rate. You run the model in your own environment on the instance you select.
Both bill per host-hour, but they use different instance sizes. Batch mode runs on 2xlarge instances for processing groups of documents at once. Real-time mode runs on xlarge instances for immediate, on-demand requests. You pick the mode that matches how your workload sends text for de-identification.
Charges accrue only while the instance runs. A stopped instance does not accrue host-hour software charges. Batch mode lets you run the instance only while processing documents, then shut it down. Real-time mode keeps an instance running to serve live requests, so it accrues charges continuously.
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
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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