In the ever-evolving landscape of data science and machine learning, having the right tools at your disposal can make all the difference. In this article, we want to shine a spotlight on two essential Python libraries that have become indispensable for data scientists and machine learning practitioners alike: Matplotlib and scikit-learn.

In part I of this article, we have already learned more about HL7, its messages, structure, segments, and fields. In this part II, we will show you how to transform proprietary/custom data into HL7 using IRIS for Health. For our sample, we used the data generated by the SYNTHEA bulk data generation project (https://synthea.mitre.org/downloads). So, we will show you how to convert 1000 patients from a CSV file to HL7 v2, using the Interoperability features of IRIS for Health.

We continue with this series of articles on LLM and RAG applications and in this article we will discuss the red boxed part of the following diagram:

InterSystems Data Platforms products allow you to export and import security settings in two different ways.

This article talks about those options:
- On the command line, using ^SECURITY
- Programmatically, using the Export and Import methods of classes in the Security package

Exporting settings on the command line (^SECURITY)

You can export everything or individual sections of the security settings.

InterSystems FAQ rubric

You can programmatically retrieve routine dates and sizes using the RoutineList query of the %Library.Routine (or just %Routine) class.

The RoutineList query has an argument, and the routine name to be searched can be specified by prefix match or middle match. (For wildcards, specify * or ?)

In the example below, *.MAC is specified as an argument.

A few weeks ago I posted an announcement about a JavaScript-based interface for our mg_web WebServer interfacing addon module. mg_web isn't just restricted to use by JavaScript developers though. Many readers will be ObjectScript developers who are more used to using CSP as their web gateway. Some may even have much older legacy WebLink-based applications (and be wondering how to support them given that IRIS does not support WebLink).

As a part of the IRIS Python 2024 contest, my colleague Damir and I went with an idea to build a platform called ShelterShare for connecting victims and volunteers for shelter requests .

Here I’ll walk you through the process of creating a simple Node/Express API and connect it to a InterSystems IRIS instance.

I won't go into much detail about how to work with any of the technologies I will mention in this tutorial but I will leave links, in case you want to learn more.

The objective here is to give you a practical guide on how to set up and connect a node.js back-end API to IRIS.

Before we get our hands dirty, make sure you have Node.js running on your machine. So I'll check:

Our company is in the process of converting our software for use in Intersystems IRIS and one of the major sections of the software makes use of a custom statically-linked C library using the $ZF("function-name") functionality. During this, I found out that the process for setting up the C library to be used within the database platform has changed significantly between Cache and IRIS.

This is my introduction to a series of posts explaining how to create an end-to-end Machine Learning system.

Starting with one problem

Our IRIS Development Community has several posts without tags or wrong tagged. As the posts keep growing the organization
of each tag and the experience of any community member browsing the subjects tends to decrease.

First solutions in mind

We can think some usual solutions for this scenario, like:

cAdvisor (short for container Advisor) analyzes and exposes resource usage and performance data from running containers. cAdvisor exposes Prometheus metrics out of the box.

https://prometheus.io/docs/guides/cadvisor/

Prometheus is integrated in SAM. This makes it possible to leverage the cAdvisor metrics and expose them via Prometheus and Grafana.

Hi Community,

Imagine enabling your application to read text to your customer? This is now possible with the new IRIS feature, Embedded Python. With this new functionality IRIS can natively run any open source or commercial Python libraries natively. gTTS (https://pypi.org/project/gTTS/) is a free library that transforms text into audio using the Google Translate service.

InterSystems has also released IRIS as containerized deployments. This post is to demonstrate how InterSystems IRIS and applications those rely on IRIS a backend can be packaged into an image and be run in other machines in containers and how simple it is to do that.

A container runs image/s which have all the necessary executables, binary code, libraries, and configuration files. And the images can be moved from one machine to another, and an images repository like Docker Hub can simplify that process.

I have used an application from Open Exchange for this demo.

Recently I needed to restore a version of a production class, which was overwritten by compilation and running UpdateProduction. As the correct version was unavailable in the source control, I used journals to restore the data. Journals store a plethora of information about what's happening in the system and are quite a powerful tool. This article explains how to work with journals to extract the data you require.