Amazon Sagemaker
Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. With Amazon SageMaker, all the barriers and complexity that typically slow down developers who want to use machine learning are removed. The service includes models that can be used together or independently to build, train, and deploy your machine learning models.

Extract clinical events and relations Free trial
By:
Latest Version:
5.5.4
Extract clinical events and relations from medical text.
Product Overview
This model can identify and contextualize clinical events entities from clinical documentation, assign assertion statuses and determine temporal relations between those. Covered entities: DATE, TIME, PROBLEM, TEST, TREATMENT, OCCURENCE, CLINICAL_DEPT, EVIDENTIAL, DURATION, FREQUENCY, ADMISSION, DISCHARGE. Relations: AFTER, BEFORE, OVERLAP Assertion statuses: absent, present, conditional, associated_with_someone_else, hypothetical, possible.
Key Data
Version
Type
Model Package
Highlights
This pipeline represents a solution designed to not only identify but also contextualize various entities within clinical documentation. It is capable of extracting entities such as DATE, TIME, PROBLEM, TEST, TREATMENT, OCCURRENCE, CLINICAL_DEPT, EVIDENTIAL, DURATION, FREQUENCY, ADMISSION, and DISCHARGE. The model also assigns assertion statuses like 'absent,' 'present,' 'conditional,' 'associated_with_someone_else,' 'hypothetical,' and 'possible' to these entities and identifies temporal relations among events, categorizing them as occurring 'AFTER,' 'BEFORE,' or in 'OVERLAP' with each other.
This model excels in complex healthcare scenarios that require a deep understanding of clinical narratives. For instance, it can be used in predictive analytics to foresee patient risks based on historical and real-time data, thereby aiding in personalized treatment planning. It also finds utility in case management, where it can map out the entire patient journey, from admission through discharge, by determining the temporal relationships between various clinical events.
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Pricing Information
Use this tool to estimate the software and infrastructure costs based your configuration choices. Your usage and costs might be different from this estimate. They will be reflected on your monthly AWS billing reports.
Contact us to request contract pricing for this product.
Estimating your costs
Choose your region and launch option to see the pricing details. Then, modify the estimated price by choosing different instance types.
Version
Region
Software Pricing
Model Realtime Inference$23.76/hr
running on ml.m4.xlarge
Model Batch Transform$47.52/hr
running on ml.m4.2xlarge
Infrastructure PricingWith Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
With Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
SageMaker Realtime Inference$0.24/host/hr
running on ml.m4.xlarge
SageMaker Batch Transform$0.48/host/hr
running on ml.m4.2xlarge
About Free trial
Try this product for 15 days. There will be no software charges, but AWS infrastructure charges still apply. Free Trials will automatically convert to a paid subscription upon expiration.
Model Realtime Inference
For model deployment as Real-time endpoint in Amazon SageMaker, the software is priced based on hourly pricing that can vary by instance type. Additional infrastructure cost, taxes or fees may apply.InstanceType | Realtime Inference/hr | |
---|---|---|
ml.m4.xlarge Vendor Recommended | $23.76 |
Usage Information
Model input and output details
Input
Summary
Format 1: Array of Text Documents { "text": [ "Text document 1", "Text document 2", ... ] } Format 2: Single Text Document { "text": "Single text document" } Format 3: JSON Lines (JSONL): Provide input in JSON Lines format, where each line is a JSON object representing a text document. {"text": "Text document 1"} {"text": "Text document 2"}
Input MIME type
application/json, application/jsonlinesSample input data
Sample notebook
Additional Resources
End User License Agreement
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Support Information
Extract clinical events and relations
For any assistance, please reach out to support@johnsnowlabs.com.
AWS Infrastructure
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