Sold by: John Snow LabsÂ
Overview
Identify socio-environmental health determinants like access to care, diet, employment, and housing from health records. Tailored for professionals and researchers, this pipeline extracts key factors influencing health in social, economic, and environmental contexts.
Process up to 2.8 M chars per hour for real-time and up to 12 M chars per hour for batch mode.
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
- This model is designed to detect and label Social Determinants of Health (SDOH) entities within text data. Social determinants of health are key factors that influence individual health outcomes, covering a wide range of social, economic, and environmental elements. The model has been trained with advanced machine learning techniques on a diverse collection of text sources.
- The model can accurately recognize and classify a wide range of SDOH entities, including but not limited to factors such as socioeconomic status, education level, housing conditions, access to healthcare services, employment status, cultural and ethnic background, neighborhood characteristics, and environmental factors. The model accuracy and precision have been carefully validated against expert-labeled data to ensure reliable and consistent results
- Covered entities: Access_To_Care, Age, Alcohol, Childhood_Event, Community_Safety, Diet, Disability, Education, Employment, Environmental_Condition, Exercise, Family_Member, Financial_Status, Food_Insecurity, Gender, Geographic_Entity, Healthcare_Institution, Housing, Hyperlipidemia, Hypertension, Income, Insurance_Status, Language, Legal_Issues, Marital_Status, Mental_Health, Obesity, Other_Disease, Population_Group, Quality_Of_Life, Race_Ethnicity, Sexual_Activity, Sexual_Orientation and more.
Details
Sold by
Delivery method
Latest version
Deployed on AWS
Unlock automation with AI agent solutions
Fast-track AI initiatives with agents, tools, and solutions from AWS Partners.
Features and programs
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
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.
Legal
Vendor terms and conditions
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA)Â .
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
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
- Array of Text Documents: { "text": [ "Text document 1", "Text document 2", ... ] }
- Single Text Document:
{ "text": "Single text document" } 3. 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_sdoh_small_en/inputs/real-time
https://github.com/JohnSnowLabs/spark-nlp-workshop/tree/master/products/sagemaker/models/explain_clinical_doc_sdoh_small_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 | 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.
Similar products
Access 2,000+ state-of-the-art models by John Snow Labs for understanding clinical and biomedical text or visual documents, using a pay-as-you-go license.
This model extracts biomarkers, classifies their presence, and links related entities in clinical text
Tavily MCP is a lightweight server that enables AI assistants to perform real time web search, intelligent crawling, and structured data extraction through the Model Context Protocol (MCP).
Tungsten Replicator for MySQL Sources is a replication engine that provides high-performance and improved replication functionality from MySQL, MariaDB & Percona Server, including Amazon RDS MySQL & Amazon Aurora.
In today's digital era, compliance with corporate transaction data is becoming increasingly important. Especially in the financial services industry, the security, legality and reliability of data are of paramount importance to enterprises. It covers a wide range of aspects, including but not limited to risk management, internal control, anti-money laundering and compliance management.
With the advancement of digital transformation, enterprises have a wider range of multiple channels for customer service. Data platforms, data types, and data quantities are also increasing rapidly. According to the requirements of compliance, the interaction, storage, query, analysis and extraction of these data have become the IT construction direction that enterprises pay attention to.
Extract text, data, photos, and signatures from all types of documents!
Customer reviews
No customer reviews yet
Be the first to review this product . We've partnered with PeerSpot to gather customer feedback. You can share your experience by writing or recording a review, or scheduling a call with a PeerSpot analyst.