Sold by: John Snow LabsÂ
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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Deployed on AWS
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Try this product free for 15 days according to the free trial terms set by the vendor.
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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
Format 1: Array of Text Documents { "text": [ "Text document 1", "Text document 2", ... ] } Format 2: Single Text Document { "text": "Single text document" } Format 3: JSON Lines (JSONL): 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
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 .
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