Sold by: John Snow LabsÂ
Deployed on AWS
Free Trial
Extract clinical events and relations from medical text.
Overview
This model can identify and contextualize clinical events entities from clinical documentation, assign assertion statuses and determine temporal relations between those. Covered entities: DATE, TIME, PROBLEM, TEST, TREATMENT, OCCURENCE, CLINICAL_DEPT, EVIDENTIAL, DURATION, FREQUENCY, ADMISSION, DISCHARGE. Relations: AFTER, BEFORE, OVERLAP Assertion statuses: absent, present, conditional, associated_with_someone_else, hypothetical, possible.
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: DATE,TIME,PROBLEM,TEST,TREATMENT,OCCURRENCE,CLINICAL_DEPT,EVIDENTIAL,DURATION,FREQUENCY, ADMISSION and DISCHARGE. The model assigns assertion statuses like 'absent,' 'present,' 'conditional,' 'associated_with_someone_else,' 'hypothetical,' and 'possible' to these entities and identifies temporal relations among events, categorizing them as occurring 'AFTER,' 'BEFORE,' or in 'OVERLAP' with each other.
- This model excels in complex healthcare scenarios that require a deep understanding of clinical narratives. For instance, it can be used in predictive analytics to foresee patient risks based on historical and real-time data, thereby aiding in personalized treatment planning. It also finds utility in case management, where it can map out the entire patient journey, from admission through discharge, by determining the temporal relationships between various clinical events.
Details
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Latest version
Deployed on AWS
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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 |
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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
New Version johnsnowlabs_version: 5.5.4 Heathcare NLP: 5.5.2 Visual NLP: 5.5.0
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.era/inputs/real-time
https://github.com/JohnSnowLabs/spark-nlp-workshop/tree/master/products/sagemaker/models/en.explain_doc.era/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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