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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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Detect cancer genetics Free trial

By:
John Snow Labs
Latest Version:
5.5.4
Extract biological and genetics entities from medical texts
Continue to Subscribe

    Product Overview

    This model extracts biological and genetics entities from medical texts to enhance therapeutic research, early diagnosis, and personalized care, driving forward data-driven medical advancements. The model was tailored to identify and extract various biological entities such as genes, anatomical systems, cellular structures, or chemicals from clinical texts, research articles, and/or pathology reports, capturing intricate biological and oncological concepts, facilitating targeted therapeutic research, early diagnosis, and personalized treatment plans. Leverage this powerful model to accelerate your data-driven medical initiatives, improve healthcare outcomes, and advance the boundaries of medical science.

    Key Data

    Version
    5.5.4
    Usage Instructions
    Release Notes
    By
    John Snow Labs
    Categories
    ML Solutions
    Names Entity Recognition - NER
    Healthcare & Life Sciences
    Type
    Model Package
    Fulfillment Methods
    Amazon SageMaker

    Highlights

      • Process up to 7 M chars per hour for real-time and up to 40 M chars per hour for batch mode.

      • Extracted entities: Amino_acid, Anatomical_system, Cancer, Cell, Cellular_component, Developing_anatomical_Structure, Gene_or_gene_product, Immaterial_anatomical_entity, Multi-tissue_structure, Organ, Organism, Organism_subdivision, Simple_chemical, Tissue

    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

    Input Format

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

    2. Array of Text Documents { "text": [

      "Text document 1",
"Text document 2",

      ] }

    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
    The document(s) to annotate must be provided as a string or as a list of strings.
    Required
    Yes
    Data type
    FreeText

    Output

    Summary

    The output is a json structure containing the original document and the entities identified by the model.

    For a complete description of the output format go here

    Output MIME type
    application/json, application/jsonlines
    Sample output data
    Expand all output descriptions
    Field name
    document
    Description
    This field refers to the full body of text that the pipeline analyzed. It includes all the content on which entity recognition is performed.
    Always returned
    Yes
    Data type
    FreeText
    Field name
    ner_chunk
    Description
    Named entity recognized in the document.
    Always returned
    Yes
    Data type
    FreeText
    Field name
    begin
    Description
    Starting character index of the named entity chunk within the document.
    Always returned
    Yes
    Data type
    Integer
    Range
    Field name
    end
    Description
    Ending character index of the named entity chunk within the document.
    Always returned
    Yes
    Data type
    Integer
    Range
    Field name
    ner_label
    Description
    Specifies the category or type of the named entity recognized in the text. The value assigned is a label that classifies the entity (ner_chunk) according to a predefined taxonomy of entity types. This classification helps in understanding the role or nature of the entity within the context of the document.
    Always returned
    Yes
    Data type
    Categorical
    Allowed values
    Amino_acid, Anatomical_system, Cancer, Cell, Cellular_component, Developing_anatomical_structure, Gene_or_gene_product, Immaterial_anatomical_entity, Multi-tissue_structure, Organ, Organism, Organism_subdivision, Organism_substance, Pathological_formation, Simple_chemical, Tissue
    Field name
    ner_confidence
    Description
    Represents the degree of certainty or confidence that the model has regarding the identification and classification of the named entity. Confidence scores are typically expressed as a number between 0 and 1, where a higher number indicates greater confidence. A score close to 1, suggests that the system is very certain of its analysis.
    Always returned
    Yes
    Data type
    Continuous (Float)
    Range
    Min: 0
    Max: 1

    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

    Detect cancer genetics

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

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    Refund Policy

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