Sold by: John Snow LabsÂ
Deployed on AWS
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Identify mental-health entities,assign assertion status and establish relations between the extracted entities
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.
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
- 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, and much more.
Details
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Pricing
Free trial
Try this product free for 15 days according to the free trial terms set by the vendor.
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.
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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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:
Version release notes
Model optimization.
Additional details
Inputs
- Summary
Input Format
- Single Text Document { "text": "Single text document" }
- Array of Text Documents { "text": [ "Text document 1", "Text document 2", ] }
- JSON Lines (JSONL) Format {"text": "Text document 1"} {"text": "Text document 2"}
- Input MIME type
- application/json, application/jsonlines
https://github.com/JohnSnowLabs/spark-nlp-workshop/tree/master/products/sagemaker/models/en.explain_doc.clinical_mental_health.pipeline/inputs/real-time
https://github.com/JohnSnowLabs/spark-nlp-workshop/tree/master/products/sagemaker/models/en.explain_doc.clinical_mental_health.pipeline/inputs/batch
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
text | Contains the text to analyze. | Type: FreeText | Yes |
Resources
Vendor resources
Support
Vendor support
For any assistance, please reach out to support@johnsnowlabs.com .
AWS infrastructure support
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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