Sold by: John Snow LabsÂ
Deployed on AWS
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Extracts entities and maps them to their ICD-10-CM codes, predicting codes up to 3 characters.
Overview
This pipeline extracts the following entities and maps them to their ICD-10-CM codes. It predicts ICD-10-CM codes up to 3 characters (according to ICD-10-CM code structure the first three characters represent the general type of injury or disease).
Predicted Entities: COMMUNICABLE_DISEASE, DIABETES, DISEASE_SYNDROME_DISORDER, EKG_FINDINGS, HEART_DISEASE, HYPERLIPIDEMIA, HYPERTENSION, IMAGINGFINDINGS, INJURY_OR_POISONING, KIDNEY_DISEASE, OBESITY, ONCOLOGICAL, OVERWEIGHT, PREGNANCY, PSYCHOLOGICAL_CONDITION, SYMPTOM, VS_FINDING
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
- Utilizing a model that extracts clinical entities and maps them to their corresponding ICD-10-CM codes enhances medical documentation and billing processes. By automatically identifying and coding diverse conditions like heart disease and diabetes up to the category level, it streamlines claims processing, improves coding accuracy, and reduces the likelihood of billing errors.
- This automation not only saves time but also ensures compliance with healthcare coding standards, enhancing revenue cycle management and allowing healthcare providers to focus more on patient care.
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
Model optimization
Additional details
Inputs
- Summary
1.Array of Text Documents { "text": [ "Text document 1", "Text document 2", ... ] }
2.Single Text Document { "text": "Single text document" }
** 3.JSON Lines (JSONL) Format** {"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/icd10cm_vdb_resolver/inputs/real-time
https://github.com/JohnSnowLabs/spark-nlp-workshop/tree/master/products/sagemaker/models/icd10cm_vdb_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 be analyzed. | 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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