Sold by: John Snow Labs
Deployed on AWS
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Extract socio-environmental health determinants
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
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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 the AWS instance running the model, with no upfront commitment. The dimensions split into two processing modes. Batch mode handles documents in bulk and runs on larger 2xlarge instances. Real-time mode processes text on demand and uses xlarge instances. Within each mode, you choose across several instance families (m, c, and r series) and generations. Your hourly rate depends on the instance size, family, and mode you pick. Larger instances and higher-generation families set different rates, so you match the instance to your throughput and memory needs.
Top-of-mind questions for buyers
You pay for the running time of the AWS instance that hosts the model, measured in host-hours. The rate covers the software license for that instance while it runs. Underlying AWS compute charges apply separately. Stopped instances stop accruing software charges, though AWS storage costs may continue.
The model reads clinical text and pulls out social determinants of health, such as housing, employment, income, and insurance status. It also marks whether each item is present, absent, or past, and links related items together. You pay by the host-hour regardless of how many documents you process.
Batch mode runs on 2xlarge instances and processes documents in bulk, which suits large jobs you run periodically. Real-time mode runs on xlarge instances and processes text on demand, which suits live requests. Each mode meters host-hours the same way, so you pick based on workload type, not billing method.
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
New Version johnsnowlabs_version: 6.4.0
Additional details
Inputs
- Summary
Model input 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/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
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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