Sold by: John Snow LabsÂ
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Identify demographic entities, social factors , medical conditions,etc. from public healthcare data and online sources.
Overview
This model is specialized in health-related text analysis in colloquial language within the domain of Public Health. It is designed to identify and extract various entities such as access to care, employment and financial Status, various social factors, substance abuse, health status, etc., informally presented in public data.
This model is tailored for gleaning crucial insights , proficiently identifying entities like gender, age, substance abuse, psychological conditions, and many more. Engineered with the healthcare provider in mind, it ensures accurate extraction of data points from social media and online sources. 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 1.8 M chars per hour for real time processing and up to 6 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: Access_To_Care, Community_Safety, Overweight, Pregnancy, Environmental_Condition, Employment, Financial_Status, Food_Insecurity, Geographic_Entity, Healthcare_Institution, Obesity ,Race_Ethnicity, Population_Group, Insurance_Status, Legal_Issues, Mental_Health, Smoking, Quality_Of_Life, Social_Exclusion, Social_Support, Spiritual_Beliefs, Substance, Violence_Or_Abuse, Education, Housing, Alcohol, Disease_Syndrome_Disorder, Diet,Relationship_Status, Drug, Alcohol, and more
- Assertion Status Labels: Hypothetical_Or_Absent, Present_Or_Past, SomeoneElse
- Relation Extraction Labels: Disease_Syndrome_Disorder-Drug, Drug-Disease_Syndrome_Disorder, Drug-Mental_Healt,Mental_Health-Drug, Allergen-Drug, Drug-Allergen, Psychological_Condition-Drug, Drug-Psychological_Condition, BMI-Obesity, Obesity-BMI , Alcohol-Substance_Quantity, Substance_Quantity-Alcohol, Smoking-Substance_Quantity, Substance_Quantity-Smoking, Substance-Substance_Quantity, Substance_Quantity-Substance
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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 |
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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.
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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/explain_clinical_doc_public_health_en/inputs/real-time
https://github.com/JohnSnowLabs/spark-nlp-workshop/tree/master/products/sagemaker/models/explain_clinical_doc_public_health_en/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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