Select your cookie preferences

We use essential cookies and similar tools that are necessary to provide our site and services. We use performance cookies to collect anonymous statistics, so we can understand how customers use our site and make improvements. Essential cookies cannot be deactivated, but you can choose “Customize” or “Decline” to decline performance cookies.

If you agree, AWS and approved third parties will also use cookies to provide useful site features, remember your preferences, and display relevant content, including relevant advertising. To accept or decline all non-essential cookies, choose “Accept” or “Decline.” To make more detailed choices, choose “Customize.”
Sign in
Sign in
or
Create a new account
About
Categories
Delivery Methods
Solutions
Resources
 Your Saved List Become a Channel Partner Sell in AWS Marketplace Amazon Web Services Home Help

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.

product logo

MeSH Sentence entity resolver Free trial

By:
John Snow Labs
Latest Version:
5.5.4
Extracts clinical entities from clinical texts and map these entities to their corresponding Medical Subject Heading (MeSH) codes.
Continue to Subscribe

    Product Overview

    This advanced pipeline extracts clinical entities from clinical texts and utilizes the sbiobert_base_cased_mli Sentence Bert Embeddings to map these entities to their corresponding Medical Subject Heading (MeSH) codes.

    Key Data

    Version
    5.5.4
    Usage Instructions
    Release Notes
    By
    John Snow Labs
    Categories
    Natural Language Processing
    Healthcare & Life Sciences
    Type
    Model Package
    Fulfillment Methods
    Amazon SageMaker

    Highlights

    • Predicted entities:

      TREATMENT, PROBLEM, TEST

    • Using a model that extracts clinical entities from texts and maps them to Medical Subject Headings (MeSH) codes greatly enhances medical documentation and research.

      It standardizes terminology in health records, improving data interoperability and retrieval across systems.

      This facilitates effective communication among healthcare providers and supports researchers in efficiently navigating vast medical literature. Automating this process reduces manual workload, allowing clinicians to dedicate more time to patient care and less to administrative tasks.

    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$47.52/hr

    running on ml.m4.2xlarge

    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$0.48/host/hr

    running on ml.m4.2xlarge

    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

    1. Array of Text Documents { "text": [ "Text document 1", "Text document 2", ... ] }

    2.Single Text Document { "text": "Single text document" }

    3.JSON Lines (JSONL) Format {"text": "Text document 1"} {"text": "Text document 2"}

    Input MIME type
    application/json, application/jsonlines
    Sample input data
    Expand all input descriptions
    Field name
    text
    Description
    Conatins the text to be analyzed.
    Required
    Yes
    Data type
    FreeText

    Output

    Summary

    To view the structure of the output format go here

    Output MIME type
    application/json, application/jsonlines
    Sample output data
    Expand all output descriptions
    Field name
    ner_chunk
    Description
    Detected NER chunk.
    Always returned
    Yes
    Data type
    FreeText
    Field name
    begin
    Description
    NER chunk begin index.
    Always returned
    Yes
    Data type
    Integer
    Range
    Field name
    end
    Description
    NER chunk end index.
    Always returned
    Yes
    Data type
    Integer
    Range
    Field name
    ner_label
    Description
    NER chunk label.
    Always returned
    Yes
    Data type
    Categorical
    Allowed values
    TREATMENT, PROBLEM, TEST
    Field name
    ner_confidence
    Description
    NER chunk confidence score.
    Always returned
    Yes
    Data type
    Continuous (Float)
    Range
    Min: 0
    Max: 1
    Field name
    concept_code
    Description
    Resolution code of the NER chunk.
    Always returned
    Yes
    Data type
    FreeText
    Field name
    resolution
    Description
    Resolution of the NER chunk.
    Always returned
    Yes
    Data type
    FreeText
    Field name
    score
    Description
    Cosine distance score of the resolution.
    Always returned
    Yes
    Data type
    Continuous (Float)
    Range
    Min: 0
    Max: 1
    Field name
    all_resolutions
    Description
    All the other possible resolutions of the NER chunk.
    Always returned
    Yes
    Data type
    FreeText
    Field name
    all_codes
    Description
    Codes of the resolutions in all_resolutions (in the same order).
    Always returned
    Yes
    Data type
    FreeText
    Field name
    all_score
    Description
    All the cosine distance scores of the all_resolutions.
    Always returned
    Yes
    Data type
    FreeText

    Sample notebook

    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

    MeSH Sentence entity resolver

    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.

    Learn More

    Refund Policy

    No refunds are possible.

    Customer Reviews

    There are currently no reviews for this product.
    View all