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Amazon Sagemaker

Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. With Amazon SageMaker, all the barriers and complexity that typically slow down developers who want to use machine learning are removed. The service includes models that can be used together or independently to build, train, and deploy your machine learning models.

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Clinical De-identification (EN) Free trial

Latest Version:
5.5.4
Mask or Obfuscate Personal Health Information (PHI) in English clinical notes.

    Product 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.

    Highlights

    • Process up to 10 M chars per hour in real-time and 18 M 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-identification process aligns with HIPAA 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, 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

    Not quite sure what you’re looking for? AWS Marketplace can help you find the right solution for your use case. Contact us

    Pricing Information

    Use this tool to estimate the software and infrastructure costs based your configuration choices. Your usage and costs might be different from this estimate. They will be reflected on your monthly AWS billing reports.

    Contact us to request contract pricing for this product.


    Estimating your costs

    Choose your region and launch option to see the pricing details. Then, modify the estimated price by choosing different instance types.

    Version
    Region

    Software Pricing

    Model Realtime Inference$23.76/hr

    running on ml.m4.xlarge

    Model Batch Transform$23.76/hr

    running on ml.m4.xlarge

    Infrastructure Pricing

    With Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
    Learn more about SageMaker pricing

    SageMaker Realtime Inference$0.24/host/hr

    running on ml.m4.xlarge

    SageMaker Batch Transform$0.24/host/hr

    running on ml.m4.xlarge

    About Free trial

    Try this product for 15 days. There will be no software charges, but AWS infrastructure charges still apply. Free Trials will automatically convert to a paid subscription upon expiration.

    Model Realtime Inference

    For model deployment as Real-time endpoint in Amazon SageMaker, the software is priced based on hourly pricing that can vary by instance type. Additional infrastructure cost, taxes or fees may apply.
    InstanceType
    Realtime Inference/hr
    ml.m4.xlarge
    Vendor Recommended
    $23.76

    Usage Information

    Model input and output details

    Input

    Summary

    The model expects as input a JSON object with three key-value pairs. The first key, "text", is associated with a string or text data that requires de-identification. The second key, "masking_policy" specifies the policy or method to be applied for de-identifying the text.

    Input MIME type
    application/json, application/jsonlines
    Sample input data

    Output

    Summary

    JSON: This pipeline outputs a JSON object that contains a key "predictions" with its value being an array. This array includes a single string elements representing de-identified text. { "predictions": [ "Output text document 1", "Output text document 2", ... ] } JSON Lines: Individual JSON objects, where each object represents predictions for a single input text. {"predictions": "Output text document 1"} {"predictions": "Output text document 1"}

    Output MIME type
    application/json, application/jsonlines
    Sample output data

    Additional Resources

    End User License Agreement

    By subscribing to this product you agree to terms and conditions outlined in the product End user License Agreement (EULA)

    Support Information

    Clinical De-identification (EN)

    For any assistance, please reach out to support@johnsnowlabs.com.

    AWS Infrastructure

    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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    Refund Policy

    No refunds are possible.

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