Amazon Sagemaker
Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. With Amazon SageMaker, all the barriers and complexity that typically slow down developers who want to use machine learning are removed. The service includes models that can be used together or independently to build, train, and deploy your machine learning models.
Predicting Cost Bloomers / Rising Risk
By:
Latest Version:
1.0
This model predicts the risk of someone currently considered low cost will become high cost over the next 12 months.
Product Overview
ClosedLoop's end to end machine learning platform allows users to go from raw, messy EHR, claims, labs, SDoH, device, and other patient linkable data streams to production deployed predictive models in just 24 hours. The predicted outcome is the probability that person’s healthcare costs in the next 12 months are 50% higher than this year’s costs, and that they exceed $10,000 in total. The algorithms were based on diagnosis and procedure data from medical claims and trained on a population of people at least 12 months of medical claims cost history.
Key Data
Version
Type
Model Package
Highlights
Simplified Data Handling - Data adapters and auto-cleaners for all major codesets, and tools to integrate person-linkable data in a HIPAA-compliant, cloud-based environment.
Automated Feature Engineering - 1000+ prebuilt, healthcare-specific features, with mappings to licensed ontologies, built-in social factors data, and custom generators for proprietary data.
Precise & Explainable Predictions - Flexible cohort and endpoint definitions, automated ML and accuracy outputs, individualized predictions and explainability factors, and dynamic ROI impact estimates.
Seamless Deployment & Management - Hosting, versioning, and automated drift and accuracy detection.
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Pricing Information
Use this tool to estimate the software and infrastructure costs based your configuration choices. Your usage and costs might be different from this estimate. They will be reflected on your monthly AWS billing reports.
Contact us to request contract pricing for this product.
Estimating your costs
Choose your region and launch option to see the pricing details. Then, modify the estimated price by choosing different instance types.
Version
Region
Software Pricing
Model Realtime Inference$1.00/hr
running on ml.t2.medium
Model Batch Transform$1.00/hr
running on ml.m5.large
Infrastructure PricingWith Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
With Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
SageMaker Realtime Inference$0.056/host/hr
running on ml.t2.medium
SageMaker Batch Transform$0.115/host/hr
running on ml.m5.large
Model Realtime Inference
For model deployment as Real-time endpoint in Amazon SageMaker, the software is priced based on hourly pricing that can vary by instance type. Additional infrastructure cost, taxes or fees may apply.InstanceType | Realtime Inference/hr | |
---|---|---|
ml.m4.4xlarge | $1.00 | |
ml.g4dn.4xlarge | $1.00 | |
ml.m5.4xlarge | $1.00 | |
ml.m4.16xlarge | $1.00 | |
ml.m5.2xlarge | $1.00 | |
ml.p3.16xlarge | $1.00 | |
ml.r5.large | $1.00 | |
ml.g4dn.2xlarge | $1.00 | |
ml.m4.2xlarge | $1.00 | |
ml.r5.12xlarge | $1.00 | |
ml.c5.2xlarge | $1.00 | |
ml.r5.xlarge | $1.00 | |
ml.p3.2xlarge | $1.00 | |
ml.c4.2xlarge | $1.00 | |
ml.g4dn.12xlarge | $1.00 | |
ml.m4.10xlarge | $1.00 | |
ml.c4.xlarge | $1.00 | |
ml.m5.24xlarge | $1.00 | |
ml.c5.xlarge | $1.00 | |
ml.g4dn.xlarge | $1.00 | |
ml.r5.24xlarge | $1.00 | |
ml.p2.xlarge | $1.00 | |
ml.m5.12xlarge | $1.00 | |
ml.g4dn.16xlarge | $1.00 | |
ml.p2.16xlarge | $1.00 | |
ml.c4.4xlarge | $1.00 | |
ml.r5.4xlarge | $1.00 | |
ml.c5.large | $1.00 | |
ml.m5.xlarge | $1.00 | |
ml.c5.9xlarge | $1.00 | |
ml.m4.xlarge | $1.00 | |
ml.c5.4xlarge | $1.00 | |
ml.p3.8xlarge | $1.00 | |
ml.c4.large | $1.00 | |
ml.m5.large | $1.00 | |
ml.c4.8xlarge | $1.00 | |
ml.p2.8xlarge | $1.00 | |
ml.g4dn.8xlarge | $1.00 | |
ml.t2.xlarge | $1.00 | |
ml.c5.18xlarge | $1.00 | |
ml.t2.large | $1.00 | |
ml.r5.2xlarge | $1.00 | |
ml.t2.medium Vendor Recommended | $1.00 | |
ml.t2.2xlarge | $1.00 |
Usage Information
Fulfillment Methods
Amazon SageMaker
You can view an example jupyter notebook using this model with the invocations endpoint here.
https://github.com/closedloop-ai/closedloop-sagemaker/blob/master/aws.sagemaker.ipynb
The model takes a JSON payload and returns a JSON response. Data represented in a tabular format where columns and rows are represented as JSON. Each JSON has the primary person id associated with it. The data required for the model consists of the following:
- personId
- gender = [male, female, other, null]
- age
- previous_cost_P-3M_to_P0D = total cost in dollars within the last 3 months.
- ed_visit_count = total count of ED visits for this person
- office_visit_count = total count of Office visits for this person
- diagnosis_CCS_X_X = true/false boolean indicating if this person has ever had a diagnosis of this CCS code.
See a full example input file here:
https://github.com/closedloop-ai/closedloop-sagemaker/blob/master/data/input/sagemaker-input.json
See a example output file that corresponds to the example input here:
https://github.com/closedloop-ai/closedloop-sagemaker/blob/master/data/output/sagemaker-output.json
The ClosedLoop.ai platform simplifies the process of data preparation for healthcare-focused predictive models. ClosedLoop makes it simple to import raw healthcare data sets, such as medical claims, prescriptions, EMR, and custom data, without the need for tedious data normalization and cleansing. If you would like to train and deploy a custom model using your own data you can try the ClosedLoop platform now at https://closedloop.ai/create-an-account/
Additional Resources
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Support Information
AWS Infrastructure
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