Sold by: Cloud Software GroupÂ
Deployed on AWS
Predicts hospital readmission rates from DRG codes, billing and EMR data.
Overview
This algorithm uses the scikit learn framework to predict hospital readmissions from EMR data, DRGs and billing data. The model predicts the probability that a patient will return to the hospital within a certain time period(30 days) or not. The predicted output is the % chance that the patient will not return/be readmitted.
Highlights
- Python, Scikit, ML, Model
Details
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Categories
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.m4.xlarge Inference (Batch) Recommended | Model inference on the ml.m4.xlarge instance type, batch mode | $0.00 |
ml.m4.xlarge Inference (Real-Time) Recommended | Model inference on the ml.m4.xlarge instance type, real-time mode | $0.00 |
ml.m4.xlarge Training Recommended | Algorithm training on the ml.m4.xlarge instance type | $0.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $0.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $0.00 |
ml.m4.10xlarge Inference (Batch) | Model inference on the ml.m4.10xlarge instance type, batch mode | $0.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $0.00 |
ml.m5.large Inference (Batch) | Model inference on the ml.m5.large instance type, batch mode | $0.00 |
ml.m5.xlarge Inference (Batch) | Model inference on the ml.m5.xlarge instance type, batch mode | $0.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $0.00 |
Vendor refund policy
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Delivery details
Amazon SageMaker algorithm
An Amazon SageMaker algorithm is a machine learning model that requires your training data to make predictions. Use the included training algorithm to generate your unique model artifact. Then deploy the 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
Beta release
Additional details
Inputs
- Summary
Requires csv with columns, 'Financial Class' (String), 'Patient Sex' (String), 'DRG Code' (Int), 'Patient Age' (Int), and Readmit.int (0 or 1).
See notebook for additional usage instructions.
- Input MIME type
- text/csv
See Input Summary
See Input Summary
Support
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.
Similar products
This model predicts the risk of a 30-day hospital readmission.
Predicts hospital readmission rate of a patient using Random Forrest algorithm
This data package contains information about hospital readmission and deaths as well as hospital excess readmission reduction program. It also includes data over hospital value based purchasing program for years 2017 and 2018. It comprises of datasets about readmission rates by age, gender, patient residence, payer, zip code and median income.
In October 2012, CMS began reducing Medicare payments for Inpatient Prospective Payment System hospitals with excess readmissions. Excess readmissions are measured by a ratio, calculated by dividing a hospital’s number of “predicted” 30-day readmissions for heart attack (AMI), heart failure (HF), pneumonia, chronic obstructive pulmonary disease (COPD), hip/knee replacement (THA/TKA), and coronary artery bypass graft surgery (CABG) by the number that would be “expected,” based on average.
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