Sold by: John Snow Labs
Deployed on AWS
Free Trial
Mask or Obfuscate Personal Health Information (PHI) in German clinical notes.
Overview
The Clinical De-Identification model is designed to recognize and anonymize PHI in German-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 10M 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:** ORGANIZATION, DOCTOR, USERNAME, CITY, DATE, COUNTRY, PROFESSION, STREET, PATIENT, PHONE, HOSPITAL, AGE, ACCOUNT, DLN, ID, PLATE, SSN, ZIP, EMAIL
- 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 model, so cost scales with how long you run inference. Dimensions split into two modes. Batch mode processes grouped records and runs on 2xlarge instances across several families (m, c, and r series). Real-time mode returns results as data arrives and runs on xlarge instances across the same families. Within each mode, you pick an instance type based on memory and processing needs; larger or newer instance families carry different hourly rates. There is no per-record or per-token charge.
Top-of-mind questions for buyers
You are billed for each hour a chosen instance runs the model, whether in batch or real-time mode. Counting is based on running time, not the number of records or documents processed. Stopped instances stop accruing software charges. Underlying AWS infrastructure fees are separate from the model's hourly rate.
Batch mode runs on 2xlarge instances and processes grouped records together, suited to large document sets. Real-time mode runs on xlarge instances and returns results as data arrives, suited to on-demand requests. Both bill by the hour. You pick the mode based on whether your workload is scheduled or interactive.
This model de-identifies protected health information in German medical texts. It masks and obfuscates entities like patient names, doctors, hospitals, dates, ages, addresses, phone numbers, IDs, and account or license numbers. The hourly rate stays the same regardless of how many entity types appear in your documents.
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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
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.
{ "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 Parameters masking_policy: str
Users can select a masking policy to determine how sensitive entities are handled:
Example: "Dr. Hans-Wolfgang Weihmann - RM57, Städt Klinikum Dresden-Friedrichstadt, Friedrichstraße 41, Dresden"
masked: Default policy that masks entities with their type.
-> 'Dr. - , , , '
obfuscated: Replaces sensitive entities with random values of the same type.
-> 'Dr. Karl-August Blümel - RP400, University Hospital Cologne, Fadime-Pölitz-Allee, Böblingen'
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/clinical_deidentification_docwise_wip_de/inputs/real-time
https://github.com/JohnSnowLabs/spark-nlp-workshop/tree/master/products/sagemaker/models/clinical_deidentification_docwise_wip_de/inputs/batch
Resources
Vendor resources
Support
Vendor support
For any assistance, please reach out to support@johnsnowlabs.com .
AWS infrastructure support
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