The InterSystems IRIS IntegratedML feature is used to get predictions and probabilities using the AutoML technique. The AutoML is a Machine Learning technology used to select the better Machine Learning algorithm/model to predict status, numbers and general results based in the past data (data used to train the AutoML model). You don't need a Data Scientist, because the AutoML it will test the most common Machine Learning algorithms and select the better algorithm to you, based in the data features analysed. See more here, in this article.

Hey Community,

Here is a short article about how to embed a video into your post.

Sometimes we need to know for sure if the current environment has sufficient cores, memory, and bandwidth to support the planned number of users and such SLAs as latency, response time, and availability. This is true for databases and backends. This is why it is mandatory for critical applications and databases to simulate the users simultaneous/concurrent requests and collect metrics about performance and availability.

InterSystems FAQ rubric

It can be obtained using AllFields query of %SYS.ProcessQuery class.

Prepare a Sample application

1. For this tutorial we will use the iris-rest-api-template (https://openexchange.intersystems.com/package/iris-rest-api-template), adding some code to install JIRISReport support. Do these steps:
2. Clone the project on a local folder:

InterSystems FAQ rubric

Data for InterSystems products (table row data, object instance data) is stored in global variables.
The data size of each global can be obtained by clicking the properties of the global you want to view from the Management Portal > System > Configuration > Local Database > Globals page, and then clicking the Calculate Size button on the Global Attributes page that appears.
To display the data sizes of globals in a namespace, you can call ^%GSIZE utility on the terminal.

Hi developers!

This will be a very short article as in April 2025 with Lovable and other Prompt-to-UI tools it becomes possible to build the frontend with prompting. Even to the folks like me who is not familiar with modern UI techics at all.

Well, I know at least the words javascript, typescript and ReactJS, so in this very short article we will be building the ReactJS UI to InterSystems FHIR server with Lovable.ai.

Let's go!

EnsLib.HL7.Message.cls provides many API methods for manipulating an HL7 message. RemoveSegmentAt(), for example, can be used to remove a segment by path or index, but only one segment at a time. There may be times that you'll need to remove all segments within a group or even many groups of segments from the HL7 message. Surely you can iterate through each segment in each group and remove them one by one, but there's a much easier way.

With just one command, like below, you can remove all OBX segments in an ORU_R01 message (msg):

Developing a Full-Stack JavaScript web app with Caché requires you to bring together the right building blocks. In this third part of this article series I will show how to link our React app to our three back-ends we created in part 2.

From the first glance, the task of configuring LDAP authentication in Caché is not hard at all – the manual describes this process in just 6 paragraphs. On the other hand, if the LDAP server uses Microsoft Active Directory, there a few non-evident things that need to be configured on the LDAP server side. Those who don’t do anything like that on a regular basis may get lost in Caché settings. In this article, we will describe the step-by-step process of setting up LDAP authentication and cover the diagnostic methods that can be used if something doesn’t work as expected.

The pandemic that struck the world in 2020 made everyone follow the news and the numbers that involve the COVID-19.

Why don’t you take that opportunity to create something simple and pleasant, to follow the number of vaccinations worldwide?

To face this challenge, I'm using the data provided by Our World in Data - Research and data to make progress against the world’s largest problems.

They have a dedicated repository on Github with the data of COVID-19, and I took the vaccination data to help me with my tracker.

You may think it isn’t too difficult to get from label+offset^routine to the actual source line responsible for the error. For an expert it isn't that hard... most of the time. But there are enough oddities and special rules that even an expert can get it wrong, whilst spending a lot of time trying to get there.

gj :: locate is the latest tool from George James Software – it debugs any error, class or routine by converting the location of an error in compiled .int code to the corresponding location in your source, and then taking you right there.

Image this scenario…

InterSystems Reports Server stores system tables in an IRIS instance of your choice. How is this configured?

Read this article if:

• You installed Logi Server directly using the Logi installer, not the InterSystems install script, OR
• You want to change the system database configuration

Effective documentation is a cornerstone of software development, aiding in code comprehension, maintenance, and collaboration. By harnessing the power of Doxygen and the ObjectScript filter I've created, you can generate rich static documentation from your source code. This approach does not require a running IRIS instance and thus is a good choice in situations when access to IRIS is not possible. Static documentation may be provided to end-users as-is, together with the source code.