Image classification & content description is incredibly powerful. Cloud-based computer vision services instantly return a JSON-based description of what they see in photos.
However, most examples are quite complex. As a beginning developer with your main knowledge in HTML + JavaScript, the following code is for you. You don’t need to worry about Node.js or native apps. The code runs directly in your browser from your computer.
Updated: January 18th, 2023 – changed info from Microsoft LUIS to Microsoft Azure Cognitive Services / Conversational Language Understanding.
In this last part, we bring the vital sign checklist to life. Artificial Intelligence interprets assessments spoken in natural language. It extracts the relevant information and manages an up-to-date, browser-based checklist. Real-time communication is handled through Web Sockets with Socket.IO.
The example scenario focuses on a vital signs checklist in a hospital. The same concept applies to countless other use cases.
In this article, we’ll query the Microsoft Azure Conversational Language Understanding Service from a Node.js backend. The results are communicated to the client through Socket.IO.
In the previous article, we verified that our language understanding service works fine. Now, it’s time to connect all components. The aim is to query our model endpoint from our Node.js backend.
Updated: December 12th, 2022 – changed info from Microsoft LUIS to Microsoft Azure Cognitive Services / Conversational Language Understanding.
Training Artificial Intelligence to perform real-life tasks has been painful. The latest AI services now offer more accessible user interfaces. These require little knowledge about machine learning. The Microsoft Azure Conversational Language Understanding Service performs an amazing task: interpreting natural language sentences and extracting relevant parts. You only need to provide 5+ sample sentences per scenario.
In this article series, we’re creating a sample app that interprets assessments from vital signs checks in hospitals. It filters out relevant information like the measured temperature or pupillary response. Yet, it’s easy to extend the scenario to any other area.
After creating the backend service and the client user interface in the first two parts, we now start setting up the actual language understanding service. I’m using the Conversational Language Understanding service from Microsoft, which is based on the Cognitive Services of Microsoft Azure.
Updated: December 12th, 2022 – changed info from Microsoft LUIS to Microsoft Azure Cognitive Services / Conversational Language Understanding.
The vision: automatic checklists, filled out by simply listening to users explaining what they observe. The architecture of the sample app is based on a lightweight architecture: HTML5, Node.js & the Microsoft Conversational Language Understanding Cognitive service in the cloud.
Such an app would be incredibly useful in a hospital, where nurses need to perform and log countless vital sign checks with patients every day.
In part 1 of the article, I’ve explained the overall architecture of the service. In this part, we get hands-on and start implementing the Node.js-based backend. It will ultimately handle all the central messaging. It communicates both with the client user interface running in a browser, as well as the Microsoft LUIS language understanding service in the Azure Cloud.
Node.js is a great fit for such a service. It’s easy to setup and uses JavaScript for development. Also, the code runs locally for development, allowing rapid testing. But it’s easy to deploy it to a dedicated server or the cloud later.
I’m using the latest version of Node.js LTS (currently version 18) and the free Visual Studio Code IDE for editing the script files.
Updated: December 12th, 2022 – changed info from Microsoft LUIS to Microsoft Azure Cognitive Services / Conversational Language Understanding.
During the last few years, cognitive services have become immensely powerful. Especially interesting is natural language understanding. Using the latest tools, training the computer to understand spoken sentences and to extract information is reduced to a matter of minutes. We as humans no longer need to learn how to speak with a computer; it simply understands us.
I’ll show you how to use the Conversational Language Understanding Cognitive Service from Microsoft. The aim is to build an automated checklist for nurses working at hospitals. Every morning, they record the vital sign of every patient. At the same time, they document the measurements on paper checklists.
With the new app developed in this article, the process is much easier. While checking the vital signs, nurses usually talk to the patients about their assessments. The “Vital Signs Checklist” app filters out the relevant data (e.g., the temperature or the pupillary response) and marks it in a checklist. Nurses no longer have to pick up a pen to manually record the information.
In this article, we’ll create a simple app that uses the conversational language understanding APIs of the Microsoft Azure Cognitive Services. The service extracts the relevant data from freely written or spoken assessments.