Sold by: John Snow LabsÂ
Deployed on AWS
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This model identifies tests, imaging techniques, and imaging findings entities from radiology reports.
Overview
This model is engineered for radiology texts and reports, adeptly identifying entities such as imaging tests, imaging techniques, imaging findings, and more. It also automatically detects the assertion status of the findings: Confirmed, Suspected, Negative, and can find relations between diagnosis, tests, and imaging findings. Developed with radiologists, technicians, and medical researchers in mind, the model brings high accuracy to the extraction of pivotal data points from radiological documentation. Harness the power of this pipeline to enhance diagnostic precision, streamline radiological workflows, and support data-driven clinical decision-making.
Highlights
- The model identifies the following entities in radiology reports: * ImagingTest, * Imaging_Technique, * ImagingFindings, * OtherFindings, * BodyPart, * Direction, * Test, * Symptom, * Disease_Syndrome_Disorder, * Medical_Device, * Procedure, * Measurements, * Units
- The model associated the following assertion statuses to the detected entities: * Confirmed, * Suspected, * Negative.
- The model identifies the relations between diagnosis, tests, and imaging findings.
Details
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Delivery method
Latest version
Deployed on AWS
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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.m4.2xlarge Inference (Real-Time) Recommended | Model inference on the ml.m4.2xlarge instance type, real-time mode | $47.52 |
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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
New Version johnsnowlabs_version: 5.5.4 Heathcare NLP: 5.5.2 Visual NLP: 5.5.0
Additional details
Inputs
- Summary
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/en.explain_doc.clinical_radiology.pipeline/inputs/real-time
https://github.com/JohnSnowLabs/spark-nlp-workshop/tree/master/products/sagemaker/models/en.explain_doc.clinical_radiology.pipeline/inputs/batch
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