Sold by: John Snow LabsÂ
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This pipeline can extract clinical conditions, and map the clinical conditions to their respective ICD-10-CM codes.
Overview
The ICD-10-CM Clinical Terminology Mapper is a comprehensive, pretrained Spark NLP pipeline designed to extract clinical conditions from unstructured medical text and map them to their corresponding ICD-10-CM codes. Utilizing advanced sentence embeddings, this pipeline facilitates accurate and standardized coding, enhancing clinical data analysis and interoperability.
Key Features
Entity Extraction: Identifies a wide range of clinical entities, including: Cerebrovascular Diseases, Communicable Diseases, Diabetes, Disease Syndromes and Disorders, EKG Findings, Heart Diseases, Hyperlipidemia, Hypertension, Imaging Findings, Injuries or Poisonings, Kidney Diseases, Obesity, Oncological Conditions, Overweight, Pregnancy-related Conditions, Psychological Conditions, Symptoms, Vital Sign Findings
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 - Billable, HCC status, and HCC score of the resolution code. See sample documentation for a complete information.
Details
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Latest version
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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
This pipeline can extract clinical conditions, and map the clinical conditions to their respective ICD-10-CM codes using sbiobert_base_cased_mli Sentence Bert Embeddings.
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/icdo_vdb_resolver/inputs/real-time
https://github.com/JohnSnowLabs/spark-nlp-workshop/tree/master/products/sagemaker/models/icdo_vdb_resolver/inputs/batch
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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