Sold by: John Snow LabsÂ
Deployed on AWS
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This model identifies diseases and syndromes entities and maps them to UMLS CUI codes.
Overview
This model is designed to identify and map diseases and syndromes mentioned in text to their respective Concept Unique Identifiers (CUI) in the Unified Medical Language System (UMLS). This model simplifies the process of medical entity coding, playing a crucial role in healthcare data standardization and interoperability.
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:** - The model accurately associates mentioned diseases and syndromes with the correct UMLS CUI codes. UMLS, a comprehensive set of healthcare terminologies, provides a unified framework for coding medical data, facilitating seamless data exchange and integration. - Designed to process various text inputs, the model can analyze clinical notes, research papers, and other medical documents, efficiently extracting and coding relevant entities.
- The model is a versatile tool that aids healthcare providers in the precise documentation of patient conditions, enhancing the efficiency of the coding process for billing and insurance claims. It plays a pivotal role in medical research by enabling the aggregation and analysis of data, due to its provision of standardized codes for diseases and syndromes. It significantly enhances healthcare data management by improving the quality and interoperability of data across various systems.
- By providing precise code mapping, the model can significantly reduce errors in medical entity coding, ensuring high-quality data for clinical and research purposes.
Details
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Delivery method
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.xlarge Inference (Batch) Recommended | Model inference on the ml.m4.xlarge instance type, batch mode | $9.84 |
ml.m4.xlarge Inference (Real-Time) Recommended | Model inference on the ml.m4.xlarge instance type, real-time mode | $9.84 |
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
This model was trained to identify diseases and syndromes entities and map those to their corresponding UMLS CUI codes. The intended audience for this context includes healthcare providers, medical coders and billers, clinical researchers, and health information managers.
Additional details
Inputs
- Summary
To use this model you need to provide input in one of the following supported formats:
- Single Text Document Provide a single text document as a string. { "text": "Single text document" }
- 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", ... ] }
- Input MIME type
- application/json
https://github.com/JohnSnowLabs/spark-nlp-workshop/tree/master/products/sagemaker/models/en.map_entity.umls_disease_syndrome_resolver/inputs/real-time
https://github.com/JohnSnowLabs/spark-nlp-workshop/tree/master/products/sagemaker/models/en.map_entity.umls_disease_syndrome_resolver/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 .
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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