Sold by: MphasisÂ
Deployed on AWS
This solution uses an unsupervised anomaly detection approach to identify suspect health metrics in a person using their smartwatch data.
Overview
This solution utilizes unsupervised outlier detection methods to detect anomalous health events from multiple sensors providing their heart rate and steps data sequenced over a period of time. Such an approach does not require labelling, as the model learns to detect anomalous values from the provided distribution itself. This provides a convenient method for preemptive analysis making use of limited, relatively easily available data. The solution is meant to provide a quick risk assessment, and should not be used as a diagnostic test. If you believe you are ill, please get medical assistance.
Highlights
- In the case of unlabelled data in the healthcare domain, it is essential to be able to accurately diagnose, analyse and determine the problem at hand, despite the lack of labels for specific training. Multiple sources of data, such as heart rate and steps such as considered here, help with increasing the feature space for better model learning. An algorithm was devised to use the different types of together, and an outlier detection model is trained on the derived data values. This model allows us to robustly detect the presence of any outliers in the multimodal data.
- This solution provides a way of preemptive evaluation given any patient's data. Detecting anomalous values with a high recall rate (minimum false negatives) which indicate the potential of the onset of a health event is quite useful as a precautionary diagnosis tool. While the final detection requires further medical tests, such solution helps identify candidates for further investigation
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Details
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Delivery method
Latest version
Deployed on AWS
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Features and programs
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
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 | $20.00 |
ml.m5.large Inference (Real-Time) Recommended | Model inference on the ml.m5.large instance type, real-time mode | $10.00 |
ml.m5.large Training Recommended | Algorithm training on the ml.m5.large instance type | $10.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $20.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $20.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $20.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $20.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $20.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $20.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $20.00 |
Vendor refund policy
Currently we do not support refunds, but you can cancel your subscription to the service at any time.
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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
bug fixes
Additional details
Inputs
- Summary
The heart rate and steps data for a single user can be uploaded either as a single file named 'hr_steps.csv' or as two different files named 'hr.csv' and 'steps.csv'. They should be formatted in the same way as the provided sample files, with the same column names for the included data.
- Input MIME type
- text/csv, application/zip
https://github.com/Mphasis-ML-Marketplace/Smartwatch-Health-Data-Anomaly-Detection/tree/main/training
https://github.com/Mphasis-ML-Marketplace/Smartwatch-Health-Data-Anomaly-Detection/tree/main/training
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
train_hr_steps.csv | • The heart rate and steps data for a single user can be uploaded either as a single file named 'hr_steps.csv' or as two different files named 'hr.csv' and 'steps.csv'.
• They should be formatted in the same way as the provided sample files, with the same column names for the included data.
• For either of the file upload formats, heart rate data should be stored under the 'heartrate' column and the steps data should be stored under 'steps' column. | Type: Continuous | Yes |
train_hr.csv | • The heart rate and steps data for a single user can be uploaded either as a single file named 'hr_steps.csv' or as two different files named 'hr.csv' and 'steps.csv'.
• They should be formatted in the same way as the provided sample files, with the same column names for the included data.
• For either of the file upload formats, heart rate data should be stored under the 'heartrate' column and the steps data should be stored under 'steps' column. | Type: Continuous | Yes |
train_steps.csv | • The heart rate and steps data for a single user can be uploaded either as a single file named 'hr_steps.csv' or as two different files named 'hr.csv' and 'steps.csv'.
• They should be formatted in the same way as the provided sample files, with the same column names for the included data.
• For either of the file upload formats, heart rate data should be stored under the 'heartrate' column and the steps data should be stored under 'steps' column. | Type: Continuous | Yes |
Resources
Vendor resources
Support
Vendor support
Mphasis
AWS infrastructure support
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