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Intelligent document processing with AWS AI and Analytics services in the insurance industry: Part 2

AWS Machine Learning

In Part 1 of this series, we discussed intelligent document processing (IDP), and how IDP can accelerate claims processing use cases in the insurance industry. Intelligent document processing with AWS AI and Analytics services in the insurance industry. Part 1: Classification and extraction of documents. Solution overview.

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Explain medical decisions in clinical settings using Amazon SageMaker Clarify

AWS Machine Learning

The intent is to offer an accelerated path to adoption of predictive techniques within CDSSs for many healthcare organizations. Technical background A large asset for any acute healthcare organization is its clinical notes. The notebook and code from this post are available on GitHub. Patient rates the pain as 8/10 in severity.

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Retain original PDF formatting to view translated documents with Amazon Textract, Amazon Translate, and PDFBox

AWS Machine Learning

Companies across various industries create, scan, and store large volumes of PDF documents. For verticals such as healthcare, due to regulatory requirements, the translated documents require an additional human in the loop to verify the validity of the machine-translated document. This is a manual, slow, and expensive human effort.

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Advanced RAG patterns on Amazon SageMaker

AWS Machine Learning

Solution overview In this post, we demonstrate the use of Mixtral-8x7B Instruct text generation combined with the BGE Large En embedding model to efficiently construct a RAG QnA system on an Amazon SageMaker notebook using the parent document retriever tool and contextual compression technique. We use an ml.t3.medium

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Identify key insights from text documents through fine-tuning and HPO with Amazon SageMaker JumpStart

AWS Machine Learning

Organizations across industries such as retail, banking, finance, healthcare, manufacturing, and lending often have to deal with vast amounts of unstructured text documents coming from various sources, such as news, blogs, product reviews, customer support channels, and social media. Healthcare and life sciences.

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Lumoa raises €650 000 seed funding to take AI-powered customer experience analytics to the new markets

Lumoa

Leading companies in healthcare, telecommunications and banking across Nordics already benefit from real time Net Promoter Score (NPS) analytics that can handle feedback in all major languages. The global customer journey analytics market size will reach $12.22 billion by 2022 growing from $4.76

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Authoring custom transformations in Amazon SageMaker Data Wrangler using NLTK and SciPy

AWS Machine Learning

You can then generate industrialized pipelines to push the features to Amazon Simple Storage Service (Amazon S3) or Amazon SageMaker Feature Store. client("comprehend") response = comprehend.detect_entities(LanguageCode = 'en', Text = df['name'].iloc[0]) The following diagram shows the end-to-end high-level architecture.