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

Extract entities from mental-health data Free trial

By:
John Snow Labs
Latest Version:
5.5.4
Identify mental-health entities,assign assertion status and establish relations between the extracted entities
Continue to Subscribe

    Product Overview

    This model is specialized in analyzing and extracting mental-health entities such as opioid drugs, substance use, substance quantity,, symptom, drug information like dosage, duration, route, form, frequency,strength, etc, procedures, treatment and test , patient social information and more. This model is tailored for gleaning crucial insights , proficiently identifying entities like gender, age, substance abuse, psychological conditions, and many more. By leveraging this pipeline, medical professionals can gain a more comprehensive understanding of the patient experience, ensuring care that is both patient-centered and data-informed. Analyse up to 1.7 M chars per hour for real time processing and up to 5.5 M chars per hour for batch processing.

    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

    • Extracted Entities: Mental_Health, Opioid_Drug, Substance_Use, Substance_Quantity, Injury_or_Poisoning, Symptom, Drug, Drug_Dosage, Drug_Duration, Drug_Route, Drug_Form, Frequency, Drug_Strength, Procedure, Treatment, Test, Test_Result, Gender, Age, Disease_Syndrome_Disorder, Smoking, Alcohol, Social_Exclusion, Family_Member, Childhood_Event, Disability, Eating_Disorder, Violence_Or_Abuse

    • Assertion Status Labels: Present, Absent, Possible, Planned, Past, Family, Hypotetical, SomeoneElse

    • Relation Extraction Labels:

      Drug,Drug_Dosage,Drug-Frequency,Drug-Duration,Drug-Drug_Strength, Test-Test_Result, Disease_Syndrome_Disorder-Symptom, Disease_Syndrome_Disorder-Drug, Symptom-Drug, Treatment_Drug, Symptom-Treatment, Disease_Syndrome_Disorder-Treatment, Treatment-Drug, Disease_Syndrome_Disorder-Procedure, Procedure-Drug, Mental_Health-Opioid_Drug, Mental_Health-Substance_Use, Mental_Health-Injury_or_Poisoning, Mental_Health-Symptom, Mental_Health-Drug, Mental_Health-Procedure, Mental_Health-Treatment, Mental_Health-Test, Mental_Health-Gender, Mental_Health-Age and much more.

    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
    Contains the text to analyze.
    Required
    Yes
    Data type
    FreeText

    Output

    Summary

    The output is a json structure containing all mental healthcare-related entities, the assertion status to the extracted entities and the relations between the extracted entities. See the details of the output structure here

    Output MIME type
    application/json, application/jsonlines
    Sample output data
    Expand all output descriptions
    Field name
    ner_chunk
    Description
    The text of the entity.
    Always returned
    Yes
    Data type
    FreeText
    Field name
    begin:
    Description
    Starting index of the named entity chunk within the document.
    Always returned
    Yes
    Data type
    Integer
    Range
    Field name
    end
    Description
    Ending index of the named entity chunk within the document.
    Always returned
    Yes
    Data type
    Integer
    Range
    Field name
    ner_label
    Description
    The label of the entity, as determined by the NER model.
    Always returned
    Yes
    Data type
    Categorical
    Allowed values
    Mental_Health, Opioid_Drug, Substance_Use, Substance_Quantity, Injury_or_Poisoning, Symptom, Drug, Drug_Dosage, Drug_Duration, Drug_Route, Drug_Form, Frequency, Drug_Strength, Procedure, Treatment, Test, Test_Result, Gender, Age, Disease_Syndrome_Disorder, Smoking, Alcohol, Social_Exclusion, Family_Member, Childhood_Event, Disability, Eating_Disorder, Violence_Or_Abuse
    Field name
    ner_confidence
    Description
    Confidence score associated with the NER prediction.
    Always returned
    Yes
    Data type
    Continuous (Float)
    Range
    Min: 0
    Max: 1
    Field name
    assertion
    Description
    Assertion status.
    Always returned
    Yes
    Data type
    Categorical
    Allowed values
    Present, Absent, Possible, Planned, Past, Family, Hypotetical, SomeoneElse
    Field name
    assertion_confidence
    Description
    Confidence score associated with the assertion prediction.
    Always returned
    Yes
    Data type
    Continuous (Float)
    Range
    Min: 0
    Max: 1
    Field name
    ner_chunk1
    Description
    First named entity involved in the relation.
    Always returned
    Yes
    Data type
    FreeText
    Field name
    ner_chunk1_begin
    Description
    Starting index of the first named entity chunk within the document.
    Always returned
    Yes
    Data type
    Integer
    Range
    Field name
    ner_chunk1_end
    Description
    Ending index of the first named entity chunk within the document.
    Always returned
    Yes
    Data type
    Integer
    Range
    Field name
    ner_label1
    Description
    Label assigned to the first named entity.
    Always returned
    Yes
    Data type
    Categorical
    Allowed values
    Mental_Health, Opioid_Drug, Substance_Use, Substance_Quantity, Injury_or_Poisoning, Symptom, Drug, Drug_Dosage, Drug_Duration, Drug_Route, Drug_Form, Frequency, Drug_Strength, Procedure, Treatment, Test, Test_Result, Gender, Age, Disease_Syndrome_Disorder, Smoking, Alcohol, Social_Exclusion, Family_Member, Childhood_Event, Disability, Eating_Disorder, Violence_Or_Abuse
    Field name
    ner_chunk2
    Description
    Second named entity involved in the relation.
    Always returned
    Yes
    Data type
    FreeText
    Field name
    ner_chunk2_begin
    Description
    Starting index of the second named entity chunk within the document.
    Always returned
    Yes
    Data type
    Integer
    Range
    Field name
    ner_chunk2_end
    Description
    Ending index of the second named entity chunk within the document.
    Always returned
    Yes
    Data type
    Integer
    Range
    Field name
    ner_label2
    Description
    Label assigned to the second named entity.
    Always returned
    Yes
    Data type
    Categorical
    Allowed values
    Mental_Health, Opioid_Drug, Substance_Use, Substance_Quantity, Injury_or_Poisoning, Symptom, Drug, Drug_Dosage, Drug_Duration, Drug_Route, Drug_Form, Frequency, Drug_Strength, Procedure, Treatment, Test, Test_Result, Gender, Age, Disease_Syndrome_Disorder, Smoking, Alcohol, Social_Exclusion, Family_Member, Childhood_Event, Disability, Eating_Disorder, Violence_Or_Abuse
    Field name
    relation
    Description
    Type of relation identified.
    Always returned
    Yes
    Data type
    Categorical
    Allowed values
    Drug- Drug_Dosage, Drug-Frequency, Drug-Duration, Drug-Drug_Strength, Drug-Drug, Test-Test_Result, Disease_Syndrome_Disorder-Symptom, Disease_Syndrome_Disorder-Drug, Symptom-Drug, Treatment_Drug, Symptom-Treatment, Disease_Syndrome_Disorder-Treatment, Treatment-Drug, Disease_Syndrome_Disorder-Procedure, Procedure-Drug, Mental_Health-Opioid_Drug, Mental_Health-Substance_Use, Mental_Health-Injury_or_Poisoning, Mental_Health-Symptom, Mental_Health-Drug, Mental_Health-Procedure, Mental_Health-Treatment, Mental_Health-Test, Mental_Health-Gender, Mental_Health-Age, Mental_Health-Disease_Syndrome_Disorder, Mental_Health-Smoking, Mental_Health-Alcohol, Mental_Health-Social_Exclusion, Mental_Health-Childhood_Event, Mental_Health-Disability, Mental_Health-Eating_Disorder, Mental_Health- Violence_Or_Abuse, Opioid_Drug-Symptom, Opioid_Drug-Procedure, Opioid_Drug-Treatment, Opioid_Drug-Disease_Syndrome_Disorder, Opioid_Drug-Smoking, Opioid_Drug-Alcohol, Opioid_Drug-Childhood_Event, Opioid_Drug-Disability, Opioid_Drug-Eating_Disorder, Opioid_Drug-Violence_Or_Abuse, Opioid_Drug-Social_Exclusion, Substance_Use-Symptom, Substance_Use-Disease_Syndrome_Disorder, Substance_Use-Smoking, Substance_Use-Alcohol, Substance_Use-Childhood_Event, Substance_Use-Disability, Substance_Use-Eating_Disorder, Substance_Use-Violence_Or_Abuse, Substance_Use-Social_Exclusion, Substance_Use-Substance_Quantity, Injury_or_Poisoning-Symptom, Injury_or_Poisoning-Procedure, Injury_or_Poisoning-Test, Injury_or_Poisoning-Childhood_Event, Injury_or_Poisoning-Disability, Symptom-Procedure, Symptom-Test, Symptom-Eating_Disorder, Procedure-Disability, Opioid_Drug-Gender, Substance_Use-Gender, Opioid_Drug-Age, Substance_Use-Age, Violence_Or_Abuse-Gender, Violence_Or_Abuse-Age, Childhood_Event-Gender, Childhood_Event-Age, Alcohol-Gender, Alcohol-Age, Smoking-Gender, Smoking-Age, Social_Exclusion-Gender, Eating_Disorder-Disease_Syndrome_Disorder, Eating_Disorder-Treatment, Eating_Disorder-Alcohol, Eating_Disorder-Smoking, Disease_Syndrome_Disorder-Test, Disease_Syndrome_Disorder-Smoking, Disease_Syndrome_Disorder-Alcohol, Disease_Syndrome_Disorder-Social_Exclusion, Disease_Syndrome_Disorder-Childhood_Event, Disease_Syndrome_Disorder-Violence_Or_Abuse, Social_Exclusion-Alcohol, Social_Exclusion-Smoking, Social_Exclusion-Childhood_Event, Social_Exclusion-Disability, Social_Exclusion-Violence_Or_Abuse, Family_Member-Mental_Health, Childhood_Event-Disability, Childhood_Event-Eating_Disorder, Childhood_Event-Violence_Or_Abuse
    Field name
    relation_confidence
    Description
    Confidence score associated with the relation prediction.
    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

    Extract entities from mental-health data

    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