Sold by: John Snow Labs
Deployed on AWS
Free Trial
Mask or Obfuscate Personal Health Information (PHI) in English clinical notes.
Overview
The Clinical De-Identification model is designed to recognize and anonymize PHI in English-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 10M chars per hour in real-time and 18M chars per hour in batch mode. Key Features: -The model is finely tuned to identify a wide range of PHI elements in medical texts, ensuring comprehensive de-identification. -The de-id process aligns with HIPAA and other healthcare privacy regulations, aiding in legal compliance and data protection. -Ideal for research, analytics, and training purposes, safe utilization of medical texts without compromising patient privacy.
- Covered entities: LOCATION, CONTACT, PROFESSION, NAME, DATE, ID, AGE, MEDICALRECORD, ORGANIZATION, HEALTHPLAN, DOCTOR, USERNAME, LOCATION-OTHER, URL, DEVICE, CITY, ZIP, STATE, PATIENT, COUNTRY, STREET, PHONE, HOSPITAL, EMAIL, IDNUM, BIOID, FAX, LOCATION_OTHER, DLN, SSN, ACCOUNT, PLATE, VIN, LICENSE, IP
- 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
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 | $23.76 |
ml.m5.xlarge Inference (Real-Time) Recommended | Model inference on the ml.m5.xlarge instance type, real-time mode | $23.76 |
ml.m4.xlarge Inference (Real-Time) | Model inference on the ml.m4.xlarge instance type, real-time mode | $23.76 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $23.76 |
ml.m6i.2xlarge Inference (Batch) | Model inference on the ml.m6i.2xlarge instance type, batch mode | $23.76 |
ml.m7i.2xlarge Inference (Batch) | Model inference on the ml.m7i.2xlarge instance type, batch mode | $23.76 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $23.76 |
ml.c6i.2xlarge Inference (Batch) | Model inference on the ml.c6i.2xlarge instance type, batch mode | $23.76 |
ml.c7i.2xlarge Inference (Batch) | Model inference on the ml.c7i.2xlarge instance type, batch mode | $23.76 |
ml.r6i.2xlarge Inference (Batch) | Model inference on the ml.r6i.2xlarge instance type, batch mode | $23.76 |
Dimensions summary
You pay by the hour for the compute instance that runs the de-identification model. Pricing is metered per host hour, so your cost scales with how long instances run. The dimensions split into two processing modes. Real-time instances handle live requests as they arrive. Batch instances process grouped records in scheduled runs and use larger instance types. Within each mode, you choose from several instance families and sizes, letting you match compute power to your workload. Larger or newer instance types carry different hourly rates. You run this on your own infrastructure, and all usage is metered.
Top-of-mind questions for buyers
One host hour is one hour that a de-identification model instance runs. Charges accrue per active instance-hour while it runs. Stopped or terminated instances stop accruing software charges. Metering sums all running hours across parallel instances into your usage bill. Underlying AWS infrastructure fees may still apply separately.
Real-time instances handle live requests as they arrive, suited to interactive or on-demand de-identification. Batch instances process grouped records in scheduled runs and use larger instance sizes, suited to bulk datasets. Both meter by host hour, so you choose the mode that fits your workload timing and volume.
No document, character, or word limit applies. You pay for instance runtime, not volume processed. The pay-as-you-go model allows parallel use across multiple machines, with all running hours metered and summed. Larger batch instance types can process more records per hour, which affects total runtime.
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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:
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Additional details
Inputs
- Summary
Input Format
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 Parameter masking_policy: str
Users can select a masking policy to determine how sensitive entities are handled:
masked: Default policy that masks entities with their type.
Example: "My name is Mike. I was admitted to the hospital yesterday." -> "My name is . I was admitted to the hospital yesterday."
obfuscated: Replaces sensitive entities with random values of the same type.
Example: "My name is Mike. I was admitted to the hospital yesterday." -> "My name is Barbaraann Share. I was admitted to the hospital yesterday."
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_en/inputs/real-time
https://github.com/JohnSnowLabs/spark-nlp-workshop/tree/master/products/sagemaker/models/clinical_deidentification_docwise_wip_en/inputs/batch
Support
Vendor support
For any assistance, please reach out to support@johnsnowlabs.com .
AWS infrastructure support
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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