Sold by: Perception HealthÂ
Overview
Using predictive modeling and machine learning techniques, CARE™ Disease Prediction analyzes more than 14 billion medical claims to identify early indicators of disease for local markets and disease onset across the United States. These predictions give you the information you need to plan outreach and prepare capacity.t in the future.
Highlights
- Predict disease before diagnosis.
- Optimize care pathways and networks to engage patients earlier.
- Save more lives.
Details
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Delivery method
Latest version
Deployed on AWS
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Pricing
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Dimension | Description | Cost/host/hour |
|---|---|---|
ml.m5.large Inference (Batch) Recommended | Model inference on the ml.m5.large instance type, batch mode | $999.00 |
ml.m5.large Inference (Real-Time) Recommended | Model inference on the ml.m5.large instance type, real-time mode | $999.00 |
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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
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}}"
- Input MIME type
- application/json, text/x-json, text/json
See Input Summary
See Input Summary
Resources
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Support
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