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NLP-Powered Data Extraction for SLRs and Meta-Analyses

Towards AI

Natural Language Processing Getting desirable data out of published reports and clinical trials and into systematic literature reviews (SLRs) — a process known as data extraction — is just one of a series of incredibly time-consuming, repetitive, and potentially error-prone steps involved in creating SLRs and meta-analyses.

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Accelerating time-to-insight with MongoDB time series collections and Amazon SageMaker Canvas

AWS Machine Learning Blog

By harnessing the transformative potential of MongoDB’s native time series data capabilities and integrating it with the power of Amazon SageMaker Canvas , organizations can overcome these challenges and unlock new levels of agility. The following diagram outlines the proposed solution architecture. Note we have two folders.

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Unlocking generative AI for enterprises: How SnapLogic powers their low-code Agent Creator using Amazon Bedrock

AWS Machine Learning Blog

Data retrieval and augmentation – When a query is initiated, the Vector Database Snap Pack retrieves relevant vectors from OpenSearch Service using similarity search algorithms to match the query with stored vectors. The retrieved vectors augment the initial query with context-specific enterprise data, enhancing its relevance.

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How MSD uses Amazon Bedrock to translate natural language into SQL for complex healthcare databases

AWS Machine Learning Blog

MSD collaborated with AWS Generative Innovation Center (GenAIIC) to implement a powerful text-to-SQL generative AI solution that streamlines data extraction from complex healthcare databases. MSD employs numerous analysts and data scientists who analyze databases for valuable insights. He received a Ph.D.

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