Sold by: ClosedLoop.aiÂ
Deployed on AWS
This model predicts the risk of receiving an initial diagnosis of diabetes over the next year.
Overview
ClosedLoop's end to end machine learning platform 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 of receiving an initial diabetes diagnosis in the next 12 months. The algorithms were based on diagnosis and procedure data from medical claims and trained on a population of people with at least 12 months of medical claims history and no diagnoses of diabetes in their medical claims history.
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.
Details
Sold by
Delivery method
Latest version
Deployed on AWS
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Pricing
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.m5.large Inference (Batch) Recommended | Model inference on the ml.m5.large instance type, batch mode | $1.00 |
ml.t2.medium Inference (Real-Time) Recommended | Model inference on the ml.t2.medium instance type, real-time mode | $1.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $1.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $1.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $1.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $1.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $1.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $1.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $1.00 |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $1.00 |
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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
This model has been optimized for maximum accuracy on Medicare Advantage members. The ClosedLoop.ai platform enables models to be custom trained for any population using all available data. Try ClosedLoop now at https://closedloop.ai/create-an-account/Â
Additional details
Inputs
- Summary
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.ipynbÂ
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.jsonÂ
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.jsonÂ
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/Â
- Input MIME type
- application/json
See Input Summary
See Input Summary
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Support
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