AWS Machine Learning Blog

Category: Amazon Comprehend

Analyze content with Amazon Comprehend and Amazon SageMaker notebooks

In today’s connected world, it’s important for companies to monitor social media channels to protect their brand and customer relationships. Companies try to learn about their customers, products, and services through social media, emails, and other communications. Machine learning (ML) models can help address some of these needs. However, the process to build and train […]

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Amazon Comprehend now supports resource tagging for custom models

Amazon Comprehend customers are solving a variety of use cases with custom classification and entity type models. For example, customers are building classifiers to organize their daily customer feedback into categories like “loyalty,” “sales,” or “product defect.” Custom entity models enable customers to analyze text for their own terms and phrases, such as product IDs […]

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Amazon Comprehend now support KMS encryption

Amazon Comprehend is a fully managed natural language processing (NLP) service that enables text analytics for important workloads. For example, analyzing market research reports for key market indicators or data that contains PII information. Customers that work with highly sensitive, encrypted data can now easily enable Comprehend to work with this encrypted data via an integration […]

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De-identify medical images with the help of Amazon Comprehend Medical and Amazon Rekognition

Medical images are a foundational tool in modern medicine that enable clinicians to visualize critical information about a patient to help diagnose and treat them. The digitization of medical images has vastly improved our ability to reliably store, share, view, search, and curate these images to assist our medical professionals. The number of modalities for […]

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Map clinical notes to the OMOP Common Data Model and healthcare ontologies using Amazon Comprehend Medical

Being able to describe the health of patients with observational data is an important aspect of our modern healthcare system. The amount of quantifiable personal health information is vast and constantly growing as new healthcare methods, metrics, and devices are introduced. All of this data allows clinicians and researchers to understand how the health of […]

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Newstag improves global video news discoverability using AI language services on AWS

Swedish startup Newstag uses artificial intelligence (AI) to allow customers to create personalized video news channels from major global news providers. Their mission is to continuously empower people and organizations with the latest, diverse information. To increase discoverability of video news from all around the world for their customers, Newstag creates rich metadata for each […]

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Bridgeman Images uses Amazon Translate to establish their business globally

Many businesses aspire to expand globally to reach new customer and accelerate growth. For Bridgeman Images, this meant engaging customers who spoke languages other than English. They needed a scalable solution to overcoming the language barrier since having everything translated manually wasn’t fast enough or cost efficient. Using Amazon Translate, they reduced the time needed […]

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Identifying and working with sensitive healthcare data with Amazon Comprehend Medical

At AWS, I regularly speak with AWS customers and AWS Partner Network (APN) partners about how they are using technology to transform human health. These companies often generate large amounts of health data that they use in a variety of applications, such as population health management and electronic health records. Developers need to find ways to use […]

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Extract and visualize clinical entities using Amazon Comprehend Medical

Amazon Comprehend Medical is a new HIPAA-eligible service that uses machine learning (ML) to extract medical information with high accuracy. This reduces the cost, time, and effort of processing large amounts of unstructured medical text. You can extract entities and relationships like medication, diagnosis, and dosage, and you can also extract protected health information (PHI). […]

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Use AWS Machine Learning to Analyze Customer Calls from Contact Centers (Part 2): Automate, Deploy, and Visualize Analytics using Amazon Transcribe, Amazon Comprehend, AWS CloudFormation, and Amazon QuickSight

In the previous blog post, we showed you how to string together Amazon Transcribe and Amazon Comprehend to be able to conduct sentiment analysis on call conversations from contact centers. Here, we demonstrate how to leverage AWS CloudFormation to automate the process and deploy your solution at scale. Solution Architecture The following diagram illustrates architecture that […]

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