Medical data is highly contextual and heavily multi-modal, in which each data silo is treated separately. To bridge different data, a knowledge graph-based approach integrates data across domains and helps represent the complex representation of scientific knowledge more naturally. For example, three components of major electronic health records (EHR) are diagnosis codes, primary notes, and specific medications. Because these are represented in different data silos, secondary use of these documents for accurately identifying patients with a specific observable trait is a crucial challenge. By connecting those different sources, subject matter experts have a richer pool of data to understand how different concepts such as diseases and symptoms interact with one another and help conduct their research.

AWS provides several Artificial Intelligence (AI) services. With AI services, you could implement some useful AI things: image and video analysis, document analysis, text to speech or speech to text translation, and so on. However, those AWS services can be used not only for enterprise applications but for your self-development applications. Applying these services we are able to implement an application to improve our foreign language skills. Let's map AWS AI services to language skills: It doesn't cover all skills but we could develop some of them this way.

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 […]

This months meetup is all about using AWS AI services and custom Machine Learning models to power your web applications! Mike Apted, Startup Solutions Architect with Amazon Web Services, is back presenting for this months meetup! RSVP ASAP and we'll see you there! Agenda: 6:00pm - Arrival, mingling, pizza eating 6:20pm - Welcome & Introductions 6:30pm - Presentation Begins 7:20pm - Q&A and Open Group Discussions 8:00pm - Event concludes Presentation Title: Using AWS AI services and custom ML models to power your web applications Presentation Summary: In this session, we will look at how you can build a brand new web application to do speech to text generation, translate text, gain insights from text, convert text to speech, and to detect objects via the Amazon Transcribe, Amazon Translate, Amazon Comprehend, Amazon Polly and Amazon Rekognition respectively. We will then use Amazon SageMaker to label, train and deploy our own model against which we will make predictions from the web application.

OK, I'll admit I didn't actually write the facial recognition bit of my app. But, I did find out how painless, quick and incredibly cheap it is to add facial recognition to your repertoire. As soon as I saw the release of Rekognition back in early December 2016, I wanted to play with it but just didn't have the opportunity at the time. A few weeks later, I read the famous post about building Jarvis from Mark Zuckerberg. And, guess what, the facial recognition part stood out to me once again.