
Overview
With a simple extract of a patients medical history, as risk score can be calculated on the patient's risk of being diagnosed with Luekemia in the next 360 days.
Highlights
- Predict disease before diagnosis.
- Optimize care pathways and networks to engage patients earlier.
- Save more lives.
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.m5.large Inference (Batch) Recommended | Model inference on the ml.m5.large instance type, batch mode | $999.99 |
ml.m5.large Inference (Real-Time) Recommended | Model inference on the ml.m5.large instance type, real-time mode | $999.99 |
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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.
Version release notes
Initial release
Additional details
Inputs
- Summary
Use the /invocations endpoint - Input (text/csv): patient_id, encounter_id, rendering_id, service_date, msdrg, diagnosis, procedure-icd10, procedure-hcpcs, charges Example: 58f8f53c,79328,B0395B16EEBF,2018-04-24,443,T1491XA,O329621,99239,288.00 Output (application/json): Content: "{"0":{"patient_id":"58f8f53c","probability":0.6232916121}}"