Sold by: John Snow LabsÂ
Deployed on AWS
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Transform clinical text into structured data with NLP, extracting entities, assigning statuses, and linking relationships
Overview
Clinical entity detection, assertion status assignment, and relation extraction are essential in medical text analysis. These techniques enable healthcare professionals, researchers, and medical NLP practitioners to derive valuable insights from clinical literature, electronic health records, and patient notes, enhancing the understanding and management of patient data.
This API uses state of the art medical models and is perfect for healthcare data analysts, clinical researchers, and healthcare AI application developers looking to extract detailed and actionable insights from unstructured clinical text.
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
- Key Features: * **Entity Recognition**: Extracts a wide array of clinical entities using state of the art medical model. * **Assertion Status** : Determines the assertion status of each entity and classifies it as hypothetical, past, planned, present, and more. * **Relation Extraction**: Links related entities, like drugs with their dosages and frequencies, test results with the tests, and more, which is crucial for building a connected data graph from disjointed text.
- Supported Labels: * Clinical Entity Labels: Age, Gender, Symptoms, Diseases, Medications, Vital Signs, and many others. * Assertion Status Labels: Hypothetical, Past, Present, Planned, and others to provide context. * Relation Extraction Labels: is_finding_of, is_date_of, is_result_of, Drug_BrandName-Dosage, Drug_BrandName-Frequency, Drug_BrandName-Route , Drug_BrandName-Strength, Drug_Ingredient-Dosage, Drug_Ingredient-Frequency, Drug_Ingredient-Route, Drug_Ingredient-Strength.
- Process up to 4 M chars per hour for real-time and up to 17 M chars per hour for batch mode. Benchmarking information : - [Entity Extraction](https://nlp.johnsnowlabs.com/2022/10/19/ner_jsl_en.html#benchmarking) - [Assertion Status](https://nlp.johnsnowlabs.com/2021/07/24/assertion_jsl_en.html#benchmarking) -[Relation Extraction](https://nlp.johnsnowlabs.com/2021/02/24/re_test_result_date_en.html)
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.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.
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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
Model optimization
Additional details
Inputs
- Summary
To use the model, you need to provide input in one of the following supported formats:
- 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): {"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.clinical_granular/inputs/real-time
https://github.com/JohnSnowLabs/spark-nlp-workshop/tree/master/products/sagemaker/models/en.explain_doc.clinical_granular/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 | Conatins the text to be analyzed. | 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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