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    Clinical De-identification for Romanian

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    Deployed on AWS
    Free Trial
    Mask or Obfuscate Personal Health Information (PHI) in Romanian clinical notes.

    Overview

    The Clinical De-Identification model is designed to recognize and anonymize PHI in Romanian-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 7M chars per hour in real-time and 16M 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: AGE, CITY, COUNTRY, DATE, DOCTOR, EMAIL, FAX, HOSPITAL, IDNUM, LOCATION-OTHER, MEDICALRECORD, ORGANIZATION, PATIENT, PHONE, PROFESSION, STREET, ZIP, ACCOUNT, LICENSE, PLATE
    • 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

    Delivery method

    Latest version

    Deployed on AWS

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    Pricing

    Free trial

    Try this product free for 15 days according to the free trial terms set by the vendor.

    Clinical De-identification for Romanian

     Info
    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.

    Usage costs (2)

     Info
    Dimension
    Description
    Cost/host/hour
    ml.m4.2xlarge Inference (Batch)
    Recommended
    Model inference on the ml.m4.2xlarge instance type, batch mode
    $47.52
    ml.m4.xlarge Inference (Real-Time)
    Recommended
    Model inference on the ml.m4.xlarge instance type, real-time mode
    $23.76

    Vendor refund policy

    No refunds are possible.

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    Legal

    Vendor terms and conditions

    Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA) .

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    Usage information

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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:
    Deploy the model as an API endpoint for your applications. When you send data to the endpoint, SageMaker processes it and returns results by API response. The endpoint runs continuously until you delete it. You're billed for software and SageMaker infrastructure costs while the endpoint runs. AWS Marketplace models don't support Amazon SageMaker Asynchronous Inference. For more information, see Deploy models for real-time inference  .
    Deploy the model to process batches of data stored in Amazon Simple Storage Service (Amazon S3). SageMaker runs the job, processes your data, and returns results to Amazon S3. When complete, SageMaker stops the model. You're billed for software and SageMaker infrastructure costs only during the batch job. Duration depends on your model, instance type, and dataset size. AWS Marketplace models don't support Amazon SageMaker Asynchronous Inference. For more information, see Batch transform for inference with Amazon SageMaker AI  .
    Version release notes

    Upgraded johnsnowlabs - 6.0.0, Spark-NLP - 6.0.0, Spark-Healthcare - 6.0.0

    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:

    Example:

    "LABORATOR RADStrada AbabeidaSacueni, 354573i, 3545730265-21011065-210110 ,OFFICE@SMURDICE@SMURD"

    masked: Default policy that masks entities with their type.

    -> 'LABORATOR RADIOLOGIE, , , , TEL : , E-MAIL: '

    obfuscated: Replaces sensitive entities with random values of the same type.

    -> 'LABORATOR RADIOLOGIE, Intrarea Diaconescu, Aiud, 302784 , TEL : 0263 144 119 , E-MAIL: jeneltudor@email.ro '

    masked_fixed_length_chars: Masks entities with a fixed length of asterisks (*).

    -> 'LABORATOR RADIOLOGIE, ****, ****, **** , TEL : **** , E-MAIL: ****'

    masked_with_chars: Masks entities with asterisks (*).

    -> 'LABORATOR RADIOLOGIE, [], [], [] , TEL : [*] , E-MAIL: [********]'

    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/ro.deid.clinical/inputs/real-time
    https://github.com/JohnSnowLabs/spark-nlp-workshop/tree/master/products/sagemaker/models/ro.deid.clinical/inputs/batch

    Resources

    Support

    Vendor support

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

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