Amazon Sagemaker
Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. With Amazon SageMaker, all the barriers and complexity that typically slow down developers who want to use machine learning are removed. The service includes models that can be used together or independently to build, train, and deploy your machine learning models.
Extract Diseases and their UMLS Codes Free trial
By:
Latest Version:
5.2.8
This model identifies diseases and syndromes entities and maps them to UMLS CUI codes.
Product 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.
Key Data
Version
Type
Model Package
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 also plays a pivotal role in medical research by enabling the aggregation and analysis of data, thanks to its provision of standardized codes for diseases and syndromes. Furthermore, it significantly enhances healthcare data management by improving the quality and interoperability of data across various systems and platforms.
By providing precise code mapping, the model can significantly reduce errors in medical entity coding, ensuring high-quality data for clinical and research purposes.
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Pricing Information
Use this tool to estimate the software and infrastructure costs based your configuration choices. Your usage and costs might be different from this estimate. They will be reflected on your monthly AWS billing reports.
Contact us to request contract pricing for this product.
Estimating your costs
Choose your region and launch option to see the pricing details. Then, modify the estimated price by choosing different instance types.
Version
Region
Software Pricing
Model Realtime Inference$9.84/hr
running on ml.m4.xlarge
Model Batch Transform$9.84/hr
running on ml.m4.xlarge
Infrastructure PricingWith Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
With Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
SageMaker Realtime Inference$0.24/host/hr
running on ml.m4.xlarge
SageMaker Batch Transform$0.24/host/hr
running on ml.m4.xlarge
About Free trial
Try this product for 15 days. There will be no software charges, but AWS infrastructure charges still apply. Free Trials will automatically convert to a paid subscription upon expiration.
Model Realtime Inference
For model deployment as Real-time endpoint in Amazon SageMaker, the software is priced based on hourly pricing that can vary by instance type. Additional infrastructure cost, taxes or fees may apply.InstanceType | Realtime Inference/hr | |
|---|---|---|
ml.m4.xlarge Vendor Recommended | $9.84 |
Usage Information
Model input and output details
Input
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/jsonSample input data
Output
Summary
The output consists of a JSON object with the following structure:
{ "predictions": [ { "document": "Text of the document 1", "ner_chunk": "Named Entity 1", "begin": Start Index, "end": End Index, "ner_label": "Label 1", "umls_code": code }, ... ] }
Output MIME type
application/jsonSample output data
Sample notebook
Additional Resources
End User License Agreement
By subscribing to this product you agree to terms and conditions outlined in the product End user License Agreement (EULA)
Support Information
Extract Diseases and their UMLS Codes
For any assistance, please reach out to support@johnsnowlabs.com.
AWS Infrastructure
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