Sold by: John Snow Labs
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.m5.2xlarge Inference (Batch) Recommended | Model inference on the ml.m5.2xlarge instance type, batch mode | $47.52 |
ml.m5.xlarge Inference (Real-Time) Recommended | Model inference on the ml.m5.xlarge instance type, real-time mode | $23.76 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $47.52 |
ml.m4.xlarge Inference (Real-Time) | Model inference on the ml.m4.xlarge instance type, real-time mode | $23.76 |
ml.m6i.2xlarge Inference (Batch) | Model inference on the ml.m6i.2xlarge instance type, batch mode | $47.52 |
ml.m7i.2xlarge Inference (Batch) | Model inference on the ml.m7i.2xlarge instance type, batch mode | $47.52 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $47.52 |
ml.c6i.2xlarge Inference (Batch) | Model inference on the ml.c6i.2xlarge instance type, batch mode | $47.52 |
ml.c7i.2xlarge Inference (Batch) | Model inference on the ml.c7i.2xlarge instance type, batch mode | $47.52 |
ml.r6i.2xlarge Inference (Batch) | Model inference on the ml.r6i.2xlarge instance type, batch mode | $47.52 |
Dimensions summary
You pay by the hour for each host you run, with no upfront commitment. Pricing splits into two usage modes. Batch mode processes stored data in bulk and offers ten instance choices across the m, c, and r instance families. Real-time mode handles live requests and offers nine instance choices across the same families. Within each mode, you pick an instance size that fits your compute, CPU, or memory needs. The 2xlarge sizes give more capacity than the xlarge sizes. Your cost scales with the number of host hours you use.
Top-of-mind questions for buyers
One host hour is one hour of a running inference instance. Charges accrue only while the instance runs. A stopped or terminated instance stops software charges. Underlying AWS storage or infrastructure fees may still apply separately, but the software meter counts running time only.
Batch mode processes stored documents in bulk, so you run instances only during processing jobs. Real-time mode handles live requests, so instances usually stay running to respond on demand. Both meter per host hour. Batch suits scheduled workloads; real-time suits continuous, on-demand extraction.
The pipeline extracts mental-health entities, assigns assertion status, and identifies relations between entities. It recognizes categories like Mental_Health, Substance_Use, Symptom, Drug, and Violence_Or_Abuse. Pricing does not change by entity type or count. You pay only for host hours used, regardless of how many entities are extracted.
nlp.johnsnowlabs.com
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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
Upgraded to johnsnowlabs Libs - 6.4.0
Additional details
Inputs
- Summary
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"}
- 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
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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