Sold by: Pattern SciencesÂ
Deployed on AWS
Simply and quickly demonstrate efficacy, safety, and superiority for drug label extensions, novel therapeutics, and generics
Overview
Our Platform leverages core competencies in processing high dimensional data sets, Causal Inference, Bayesian and Machine Learning for the specific needs of Pharma and Healthcare by addressing specific top of mind use cases. These are built on a foundation of establishing Cohort Equivalency that brings the rigor of traditional randomized clinical trials to RWD. More specifically, we address limitations of Propensity Scoring when it comes to High Dimension Data.
Highlights
- The Pattern Sciences’ Platform super charges pharmaceutical development by providing pre-built playbooks and optimizations for specific use cases. Use Cases include: - Synthetic Control Arms for Clinical Trials - Comparative Drug Analysis to meet the threat of Generics (Two Arms Synthetic) - Showing drug efficacy over others pre or post FDA approvals (Two+ Arms) - Intervention Analysis for Value Based Care - Off Label Effectiveness Analysis - Population Equivalency - Label Expansions - Identify and validate novel drug or biomarkers for future therapeutics
Details
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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.t2.medium Inference (Real-Time) Recommended | Model inference on the ml.t2.medium instance type, real-time mode | $0.00 |
ml.m5.large Inference (Batch) Recommended | Model inference on the ml.m5.large instance type, batch mode | $0.00 |
ml.m4.4xlarge Inference (Real-Time) | Model inference on the ml.m4.4xlarge instance type, real-time mode | $0.00 |
ml.t2.xlarge Inference (Real-Time) | Model inference on the ml.t2.xlarge instance type, real-time mode | $0.00 |
ml.m4.10xlarge Inference (Real-Time) | Model inference on the ml.m4.10xlarge instance type, real-time mode | $0.00 |
ml.t2.large Inference (Real-Time) | Model inference on the ml.t2.large instance type, real-time mode | $0.00 |
ml.t2.2xlarge Inference (Real-Time) | Model inference on the ml.t2.2xlarge instance type, real-time mode | $0.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $0.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $0.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $0.00 |
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This product is offered for free. If there are any questions, please contact us for further clarifications.
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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
Initial Version
Additional details
Inputs
- Summary
The input for the Cohort Equivalence package is of application/json. The json has two objects an X and y. The X is a dataset which used to predict whether the two population cohorts are equivalent, the y denotes the cohort either 1 or a 0.
The type of X is a list of lists of double. The y is a list of population group of either 1 or 0 denoting which group it belongs to
- Limitations for input type
- For input data, the X inner list should be a length of 12.
https://bitbucket.org/nathesh92/cohort_equivalency/src/master/test_data.json
https://bitbucket.org/nathesh92/cohort_equivalency/src/master/test_data.json
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
X | This a list of length k, where each element of the list is a list of length 12. The list of length 12 are the features corresponding to the cohort population. | Type: Continuous
Minimum: -99
Maximum: 99 | Yes |
y | This is a list of length k, where each element of the list corresponds to cohort group. The valid values for this are either 0 or 1. | Type: Integer
Minimum: 0
Maximum: 1 | Yes |
Resources
Vendor resources
Support
Vendor support
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