
Overview
This pipeline extracts oncological entities from clinical texts and map them to their corresponding ICD-O codes.
The model identifies oncological entities and maps them to the most relevant ICD-O codes, offering a high level of detail, especially concerning body part references. The model also returns the original Topography (site of the tumor) and Histology (tumor characteristics) codes, along with their detailed descriptions. This feature ensures that the coding reflects the specific nature and location of the cancer.
It provides the top terms and resolutions associated with each identified entity, allowing for a comprehensive understanding of the cancer's characteristics and its coding implications.
By facilitating the accurate and granular ICD-O coding, the model significantly improves the precision and quality of oncological clinical documentation.
This model assists in medical billing and coding by providing a solution for accurate and comprehensive coding related to oncology, primarily for billing and insurance purposes. It aids in aggregating and analyzing oncological data for clinical research, leveraging precise and standardized coding. Additionally, it supports healthcare professionals in maintaining detailed and accurate medical records for cancer patients, contributing to improved healthcare documentation in oncology.
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
- Simply pass in one or more text documents and get back : - Detected Named Entity Recognition (NER) chunk - NER chunk Position, Label and Confidence Score - Resolution and Resolution code of NER chunk - Cosine distance score of the resolution - All the other possible resolutions of the NER chunk - Codes of all resolutions - All the cosine distance scores of the for all resolutions
- Process up to 3 M chars per hour in real-time and 10 M chars per hour in batch mode.
Details
Introducing multi-product solutions
You can now purchase comprehensive solutions tailored to use cases and industries.
Features and programs
Financing for AWS Marketplace purchases
Pricing
Free trial
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.
How can we make this page better?
Legal
Vendor terms and conditions
Content disclaimer
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.
Version release notes
johnsnowlabs_version: 6.0.4 Spark-NLP==6.0.4 Spark-Healthcare==6.0.4
Additional details
Inputs
- Summary
Input Format
To use the model, you need to provide input in one of the following supported formats: JSON 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
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.
Similar products

