Sold by: Virtusa Corporation - vLife
Overview
Effective scheduling for hospital admission is a major challenge as there is uncertainty in patient’s length of stay and large errors in estimations can lead to capacity pressures. To tackle this, Virtusa-GCTS has developed a Deep Learning based solution which will accurately predict how long a newly admitted patient will stay in the hospital. The model uses cutting edge regression algorithms. This model not only helps Hospitals to plan and deploy their resources effectively but also helps insurance companies to plan their indemnities and also assist in preventing frauds.
Highlights
- Solution leverages Patient clinical history with flags for asthma, renal diseases and other vital information such as BMI, pulse etc.
- Solution can be leveraged both by hospitals and Insurance companies.
Details
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Deployed on AWS
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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 | $0.00 |
ml.t2.medium Inference (Real-Time) Recommended | Model inference on the ml.t2.medium instance type, real-time mode | $0.00 |
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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.
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Version release notes
Effective scheduling for hospital admission is a major challenge as there is uncertainty in patient’s length of stay and large errors in estimations can lead to capacity pressures. To tackle this, Virtusa-Xlabs has developed a Deep Learning based solution which will accurately predict how long a newly admitted patient will stay in the hospital. The model uses cutting edge regression algorithms. This model not only helps Hospitals to plan and deploy their resources effectively but also helps insurance companies to plan their indemnities and also assist in preventing frauds.
Additional details
Inputs
- Summary
Patient clinical history with flags for asthma, renal diseases, including psychological condition history and Patient vital information such as BMI, pulse, respiration etc. recorded at the time of admission are used for the purpose of predicting length of stay.
- Input MIME type
- text/csv
https://aws-marketplace-models.s3-us-west-2.amazonaws.com/length-of-stay/sample+notebook+/data/input/single_row.csv
https://aws-marketplace-models.s3-us-west-2.amazonaws.com/length-of-stay/sample+notebook+/data/input/multiple.csv
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
rcount | Number of readmissions within last 180 days | Type: Categorical
Allowed values: 0,1,2,3,4,5+ | Yes |
gender | Gender of the patient - M or F | Type: Categorical
Allowed values: M,F | Yes |
dialysisrenalendstage | Flag for renal disease during encounter | Type: Integer | Yes |
asthma | Flag for asthma during encounter | Type: Integer | Yes |
irondef | Flag for iron deficiency during encounter | Type: Integer | Yes |
pneum | Flag for pneumonia during encounter | Type: Integer | Yes |
substancedependence | Flag for substance dependence during encounter | Type: Integer | Yes |
psychologicaldisordermajor | Flag for major psychological disorder during encounter | Type: Integer | Yes |
depress | Flag for depression during encounter | Type: Integer | Yes |
psychother | Flag for other psychological disorder during encounter | Type: Integer | Yes |
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