Sold by: John Snow LabsÂ
Deployed on AWS
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Extract ,assign assertion and create relations between the extracted entities from the clinical documents, for 50+ oncology-related entities
Overview
This model extracts more than 50 oncology-related entities, assign assertions and creates relations between the extracted entities.
The extracted entities includes therapies, tests, staging, histological type, oncogene, and radiation dose from clinical documentation, pathology, and diagnostic reports, optimizing oncology workflows and advancing personalized cancer treatments.
Process up to 7.5 M chars per hour for real-time and up to 14 M chars per hour for batch mode.
Highlights
- **Extracted entities:** Adenopathy, Age, Biomarker, Biomarker_Result, Cancer_Dx, Cancer_Score, Cancer_Surgery, Chemotherapy, Cycle_Count, Cycle_Day, Cycle_Number, Date, Death_Entity, Direction, Dosage, Duration, Frequency, Gender, Grade, Histological_Type, Hormonal_Therapy, Imaging_Test, Immunotherapy, Invasion, Line_Of_Therapy, Metastasis, Oncogene, Pathology_Result, Pathology_Test, Performance_Status, Race_Ethnicity, Radiation_Dose, Radiotherapy, Relative_Date, Response_To_Treatment, Route, Site_Bone, Site_Brain, Site_Breast, Site_Liver, Site_Lung, Site_Lymph_Node, and much more.
- **Assertion Status Labels:** Present, Absent, Possible, Past, Family, Hypotetical
- **Relation Extraction Labels:** is_size_of, is_finding_of, is_date_of, Date-Cancer_Dx, Tumor_Finding-Site_Breast, Tumor_Finding-Site_Bone, Tumor_Finding-Site_Liver, Tumor_Finding-Site_Lung, Tumor_Finding-Site_Lymph_Node, Tumor_Finding-Site_Other_Body_Part, Tumor_Fiding-Relative_Date, Tumor_Finding-Tumor_Size, Pathology_Test-Cancer_Dx, Pathology_Test-Pathology_Result, Biomarker_Result-Biomarker, Biomarker-Biomarker_Quant, Cancer_Dx-Hormonal_Therapy, Cancer_Dx-Immunotherapy, Cancer_Dx-Radiotherapy, Cancer_Dx-Chemotherapy, Cancer_Dx-Targeted_Therapy, Cancer_Dx-Cancer_Surgery, and much more.
Details
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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 posible.
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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/explain_clinical_doc_cancer_type_en/inputs/real-time
https://github.com/JohnSnowLabs/spark-nlp-workshop/tree/master/products/sagemaker/models/explain_clinical_doc_cancer_type_en/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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