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% . In additon, the report concludes that "burnout and satisfaction with work-life balance in US physicians worsened from 2011 to 2014. More than half of US physicians are now experiencing professional burnout." As technologists, we're looking for ways to put new and innovative solutions in the hands of physicians to make them more efficient, reduce burnout, and improve care quality.
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). Using Amazon Comprehend Medical allows end users to get value from raw clinical notes that is otherwise largely unused for analytical purposes because it's difficult to parse. There is immense value associated with extracting information from these notes and integrating it with other medical systems like an Electronic Health Record (EHR) and a Clinical Trial Management System (CTMS).
In this blog post we'll show you how you can use Amazon Translate, Amazon Comprehend, Amazon Kinesis, Amazon Athena, and Amazon QuickSight to build a natural-language-processing (NLP)-powered social media dashboard for tweets. These conversations are a low-cost way to acquire leads, improve website traffic, develop customer relationships, and improve customer service. In this blog post, we'll build a serverless data processing and machine learning (ML) pipeline that provides a multi-lingual social media dashboard of tweets within Amazon QuickSight. We'll leverage API-driven ML services that allow developers to easily add intelligence to any application, such as computer vision, speech, language analysis, and chatbot functionality simply by calling a highly available, scalable, and secure endpoint. These building blocks will be put together with very little code, by leveraging serverless offerings within AWS.
Amazon announced the general availability of AWS Lambda support for Amazon Elastic File System. Amazon EFS is a fully managed, elastic, shared file system and designed to be consumed by other AWS services. With the release of Amazon EFS for Lambda, we can now easily share data across function invocations. It also opens new capabilities, such as building/importing large libraries and machine learning models directly into Lambda functions. Let's go over how to build a serverless conversational AI chatbot using Lambda function and EFS.
Amazon Comprehend now supports Amazon Virtual Private Cloud (Amazon VPC) endpoints via AWS PrivateLink so you can securely initiate API calls to Amazon Comprehend from within your VPC and avoid using the public internet. Amazon Comprehend is a fully managed natural language processing (NLP) service that uses machine learning (ML) to find meaning and insights in text. You can use Amazon Comprehend to analyze text documents and identify insights such as sentiment, people, brands, places, and topics in text. Using AWS PrivateLink, you can access Amazon Comprehend easily and securely by keeping your network traffic within the AWS network, while significantly simplifying your internal network architecture. It enables you to privately access Amazon Comprehend APIs from your VPC in a scalable manner by using interface VPC endpoints.