AWS Machine Learning Blog

Category: Amazon Comprehend Medical

Build an intelligent search solution with automated content enrichment

Unstructured data belonging to the enterprise continues to grow, making it a challenge for customers and employees to get the information they need. Amazon Kendra is a highly accurate intelligent search service powered by machine learning (ML). It helps you easily find the content you’re looking for, even when it’s scattered across multiple locations and […]

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Perform medical transcription analysis in real-time with AWS AI services and Twilio Media Streams

Medical providers often need to analyze and dictate patient phone conversations, doctors’ notes, clinical trial reports, and patient health records. By automating transcription, providers can quickly and accurately provide patients with medical conditions, medication, dosage, strength, and frequency. Generic artificial intelligence-based transcription models can be used to transcribe voice to text. However, medical voice data […]

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You already know how to use Amazon Athena to transform data in Amazon S3 using simple SQL commands

Translate, redact, and analyze text using SQL functions with Amazon Athena, Amazon Translate, and Amazon Comprehend

Update April 5th 2021: Post updated per Amazon Athena UDF SQL syntax updates. You have Amazon Simple Storage Service (Amazon S3) buckets full of files containing incoming customer chats, product reviews, and social media feeds, in many languages. Your task is to identify the products that people are talking about, determine if they’re expressing happy […]

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Deploying and using the Document Understanding Solution

Based on our day to day experience, the information we consume is entirely digital. We read the news on our mobile devices far more than we do from printed copy newspapers. Tickets for sporting events, music concerts, and airline travel are stored in apps on our phones. One could go weeks or longer without needing […]

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Building a medical image search platform on AWS

Improving radiologist efficiency and preventing burnout is a primary goal for healthcare providers. A nationwide study published in Mayo Clinic Proceedings in 2015 showed radiologist burnout percentage at a concerning 61% [1]. In additon, the report concludes that “burnout and satisfaction with work-life balance in US physicians worsened from 2011 to 2014. More than half […]

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Query drug adverse effects and recalls based on natural language using Amazon Comprehend Medical

In this post, we demonstrate how to use Amazon Comprehend Medical to extract medication names and medical conditions to monitor drug safety and adverse events. Amazon Comprehend Medical is a natural language processing (NLP) service that uses machine learning (ML) to easily extract relevant medical information from unstructured text. We query the OpenFDA API (an open-source API published by […]

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Analyzing and tagging assets stored in Veeva Vault PromoMats using Amazon AI services

Veeva Systems is a provider of cloud-based software for the global life sciences industry, which offers products that serve multiple domains ranging from clinical, regulatory, quality, and more. Veeva’s Vault Platform manages both content and data in a single platform that allows you to deploy powerful applications that manage the end-to-end process with content, data, […]

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Performing medical transcription analysis with Amazon Transcribe Medical and Amazon Comprehend Medical

December 2020 Update – This blog post now also covers how the Medical Transcription Analysis can also be used to store and retrieve medical transcriptions and relevant information using Amazon DynamoDB and Amazon S3 and how all of this data can be analyzed using Amazon Athena. The healthcare industry is a highly regulated and complex […]

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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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