Sold by: John Snow LabsÂ
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The SNOMED Clinical Terminology Mapper pipeline is designed to extract and normalize clinical entities from unstructured medical text.
It identifies a wide range of clinical entities and maps them to their corresponding SNOMED codes .
This facilitates standardized data representation, enabling efficient clinical data analysis and interoperability.
Overview
The SNOMED Resolver Pipeline is designed to extract and normalize clinical entities from unstructured medical text. It identifies a wide range of clinical entities and maps them to their corresponding SNOMED codes. This facilitates standardized data representation, enabling efficient clinical data analysis and interoperability.
Key Features
Entity Extraction: Identifies various clinical entities, including:
Clinical Findings, Morphological Abnormalities, Clinical Drugs and Drug Forms, Procedures, Substances, Physical Objects, Body Structures.
SNOMED Mapping: Maps extracted entities to their corresponding SNOMED codes ensuring standardized terminology.
High Accuracy: Utilizes advanced biomedical embeddings to achieve precise concept resolution, enhancing the reliability of extracted data.
Scalability: Built on Apache Spark, the pipeline supports large-scale processing of clinical documents, making it suitable for enterprise-level applications.
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
- The pipeline accepts a single text document or an array of text documents or JSON Lines (JSONL) format as input.
- The pipeline returns information in JSON format, containing: * Detected named entity resolution (NER) chunk * position of the detected NER chunk in the document * NER chunk label. * NER chunk confidence score. * Resolution code of the NER chunk. * Resolution of the NER chunk. * Score, representing cosine distance score of the resolution. Refer to the sample documentation for a detailed explanation of the returned result structure.
Details
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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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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
The SNOMED Resolver Pipeline is designed to extract and normalize clinical entities from unstructured medical text. It identifies a wide range of clinical entities and maps them to their corresponding SNOMED codes using sbiobert_base_cased_mli embeddings. This facilitates standardized data representation, enabling efficient clinical data analysis and interoperability.
SPARK NLP HC VERSION 5.3.0
Additional details
Inputs
- Summary
Input Format
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/snomed_vdb_resolver/inputs/real-time
https://github.com/JohnSnowLabs/spark-nlp-workshop/tree/master/products/sagemaker/models/snomed_vdb_resolver/inputs/batch
Resources
Vendor resources
Support
Vendor support
For any assistance, please reach out to support@johnsnowlabs.com .
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