Sold by: John Snow Labs
Deployed on AWS
Free Trial
Mask or Obfuscate Personal Health Information (PHI) in French clinical notes.
Overview
The Clinical De-Identification model is designed to recognize and anonymize PHI in French-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 14M chars per hour in real-time and 25M 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: DATE, AGE, SEX, PROFESSION, ORGANIZATION, PHONE, E-MAIL, ZIP, STREET, CITY, COUNTRY, PATIENT, DOCTOR, HOSPITAL, MEDICALRECORD, SSN, IDNUM, ACCOUNT, PLATE, USERNAME, URL, and IPADDR.
- 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 de-identification, so cost tracks how long the model runs. Pricing splits into two processing modes. Batch mode covers scheduled jobs on 2xlarge instances across several instance families. Real-time mode covers live requests on xlarge instances across similar families. Within each mode, you choose an instance family and size (general-purpose, compute-optimized, or memory-optimized) to match your workload. Larger or specialized instances handle heavier loads. There is no per-token or per-record fee; you are billed only for instance host hours used.
Top-of-mind questions for buyers
Batch mode meters instance-hours for scheduled jobs that process stored records in bulk on 2xlarge instances. Real-time mode meters instance-hours for live requests that de-identify text as it arrives on xlarge instances. Both bill only for the hours the instance runs. Choose batch for continuous large datasets, real-time for on-demand requests.
You pay per host hour only while the instance runs. Software charges stop when the instance is stopped or powered off. Underlying AWS storage or infrastructure fees may still apply separately, but the de-identification software meters running time alone. There is no per-token or per-record charge.
No. Cost tracks instance host hours, not record or token volume. Whether you process one note or many during a running hour, the hourly rate stays the same. Your bill scales with how long the instance runs and which instance size you select, not throughput count.
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
Upgraded to johnsnowlabs Libs - 6.4.0
Additional details
Inputs
- Summary
Input Format
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.
{ "text": [ "Text document 1", "Text document 2", ... ] } 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: "PRENOM : Éric, NOM : Lejeune, NUMÉRO DE SÉCURITÉ SOCIALE : 2730235238020, ADRESSE : 74 Boulevard Riou, VILLE : Sainte Annenec"
masked: Default policy that masks entities with their type.
-> 'PRENOM : , NOM : , NUMÉRO DE SÉCURITÉ SOCIALE : , ADRESSE : , VILLE : '
obfuscated: Replaces sensitive entities with random values of the same type.
-> 'PRENOM : Mlle Goncalves, NOM : Mme Garnier-Roussel, NUMÉRO DE SÉCURITÉ SOCIALE : 129022554007724, ADRESSE : avenue de Mahe, VILLE : Meyer-sur-Lemoine'
masked_fixed_length_chars: Masks entities with a fixed length of asterisks (*).
-> 'PRENOM : ****, NOM : ****, NUMÉRO DE SÉCURITÉ SOCIALE : ****, ADRESSE : ****, VILLE : ****'
masked_with_chars: Masks entities with asterisks (*).
-> 'PRENOM : [], NOM : [], NUMÉRO DE SÉCURITÉ SOCIALE : [****], ADRESSE : [*******], VILLE : [********]'
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/fr.deid_obfuscated/inputs/real-time
https://github.com/JohnSnowLabs/spark-nlp-workshop/tree/master/products/sagemaker/models/fr.deid_obfuscated/inputs/batch
Resources
Vendor resources
Support
Vendor support
For any assistance, please reach out to support@johnsnowlabs.com .
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