Article
· Nov 15, 2024 2m read
HL7 -> SDA -> FHIR -> Personal Growth

I have a large tree outside my window. Here in Phoenix, Arizona, USA it's still warm and sunny so our plants are still covered in lush foliage despite so many other places in the full throws of Autumn. My desk is oriented such that I can look out at the tree throughout the day.

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Hi, Community!

In the previous article, we introduced the Streamlit web framework, a powerful tool that enables data scientists and machine learning engineers to build interactive web applications with minimal effort. First, we explored how to install Streamlit and run a basic Streamlit app. Then, we incorporated some of Streamlit's basic commands, e.g., adding titles, headers, markdown, and displaying such multimedia as images, audio, and videos.

Later, we covered Streamlit widgets, which allow users to interact with the app through buttons, sliders, checkboxes, and more. Additionally, we examined how to display progress bars and status messages and organize the app with sidebars and containers. We also highlighted data visualization, using charts and Matplotlib figures to present data interactively.

In this article, we will cover the following topics:

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Here at InterSystems, we often deal with massive datasets of structured data. It’s not uncommon to see customers with tables spanning >100 fields and >1 billion rows, each table totaling hundred of GB of data. Now imagine joining two or three of these tables together, with a schema that wasn’t optimized for this specific use case. Just for fun, let’s say you have 10 years worth of EMR data from 20 different hospitals across your state, and you’ve been tasked with finding….

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Hi, Community!

In this article, I will introduce Python Streamlit Web Framework.

Below, you can find the topics we will cover:

  • 1-Introduction to Streamlit Web Framework
  • 2-Installation of Streamlit module
  • 3-Running Streamlit Application
  • 4-Streamlit Basic commands
  • 5-Display multimedia
  • 6-Input widgets
  • 7-Display progress and status
  • 8-Sidebar and container
  • 9-Data Visualization
  • 10-Display a DataFrame

So, let's start with the first topic.

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Article
· Sep 12, 2024 7m read
Embedded python in InterSystems IRIS

Hello Community,

In this article, I will outline and illustrate the process of implementing ObjectScript within embedded Python. This discussion will also reference other articles related to embedded Python, as well as address questions that have been beneficial to my learning journey.

As you may know, the integration of Python features within IRIS has been possible for quite some time. This article will focus on how to seamlessly incorporate ObjectScript with embedded Python.

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Greetings dear community members!

I have recently been deploying an IRIS for Health image on a Docker with a preconfigured Webgateway image and I have come across the problem of the SSL configurations that allow us to connect to the IRIS instance using HTTPS and going through our Webgateway.

Until now I had always deployed IRIS for Health with a Community license, which still has the Private Web Server installed, so I only needed to configure the Webgateway connection with the deployed IRIS instance:

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In the previous article we presented the d[IA]gnosis application developed to support the coding of diagnoses in ICD-10. In this article we will see how InterSystems IRIS for Health provides us with the necessary tools for the generation of vectors from the ICD-10 code list using a pre-trained language model, its storage and the subsequent search for similarities on all these generated vectors.

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Using SQL Gateway with Python, Vector Search, and Interoperability in InterSystems Iris

Part 3 – REST and Interoperability

Now that we have finished the configuration of the SQL Gateway and we have been able to access the data from the external database via python, and we have set up our vectorized base, we can perform some queries. For this in this part of the article we will use an application developed with CSP, HTML and Javascript that will access an integration in Iris, which then performs the search for data similarity, sends it to LLM and finally returns the generated SQL. The CSP page calls an API in Iris that receives the data to be used in the query, calling the integration. For more information about REST in the Iris see the documentation available at https://docs.intersystems.com/irislatest/csp/docbook/DocBook.UI.Page.cls...

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Article
· Apr 1 1m read
IRIS-Intelligent Butler

# IRIS-Intelligent Butler
IRIS Intelligent Butler is an AI intelligent butler system built on the InterSystems IRIS data platform, aimed at providing users with comprehensive intelligent life and work assistance through data intelligence, automated decision-making, and natural interaction.
## Application scenarios
adding services, initializing configurations, etc. are currently being enriched
## Intelligent Butler

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image

Hi Community,

In this article, I will introduce my application iris-RAG-Gen .

Iris-RAG-Gen is a generative AI Retrieval-Augmented Generation (RAG) application that leverages the functionality of IRIS Vector Search to personalize ChatGPT with the help of the Streamlit web framework, LangChain, and OpenAI. The application uses IRIS as a vector store.

Application Features

  • Ingest Documents (PDF or TXT) into IRIS
  • Chat with the selected Ingested document
  • Delete Ingested Documents
  • OpenAI ChatGPT

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The Istio Service Mesh is commonly used to monitor communication between services in applications. The "battle-tested" sidecar mode is its most common implementation. It will add a sidecar container to each pod you have in your namespace that has Istio sidecar injection enabled.

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Introduction

To achieve optimized AI performance, robust explainability, adaptability, and efficiency in healthcare solutions, InterSystems IRIS serves as the core foundation for a project within the x-rAI multi-agentic framework. This article provides an in-depth look at how InterSystems IRIS empowers the development of a real-time health data analytics platform, enabling advanced analytics and actionable insights. The solution leverages the strengths of InterSystems IRIS, including dynamic SQL, native vector search capabilities, distributed caching (ECP), and FHIR interoperability. This innovative approach directly aligns with the contest themes of "Using Dynamic SQL & Embedded SQL," "GenAI, Vector Search," and "FHIR, EHR," showcasing a practical application of InterSystems IRIS in a critical healthcare context.

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As we all know, InterSystems is a great company.

Their products can be just as useful as they are complex.

Yet, our pride sometimes prevents us from admitting that we might not understand some concepts or products that InterSystems offers for us.

Today we are beginning a series of articles explaining how some of the intricate InterSystems products work, obviously simply and clearly.

In this essay, I will clarify what Machine Learning is and how to take advantage of it.... because this time, you WILL KNOW for sure what I am talking about.

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Article
· Dec 9, 2024 40m read
Reviews on Open Exchange - #Rollup

In principle I'm looking in forward direction.
Though from time to time it makes sense to turn back and see the full path taken.

  • I started reporting my reviews almost 4 years ago.
  • I just reviewed packages that I could reproduce in my environment.
  • Skipping items beyond my physical or mental capacities.
  • So some great packages might not be present here
  • But I added also 139 contributions of other reviewers
  • It was anyhow a total of 769 reviews published
  • Meeting over this time 191 different authors

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Hey, community! 👋

We are a team of Stanford students applying technology to make sense of climate action. AI excites us because we know we can quickly analyze huge amounts of text.

As we require more reports on sustainability, such as responsibility reports and financial statements, it can be challenging to cut through the noise of aspirations and get to the real action: what are companies doing

That’s why we built a tool to match companies with climate actions scraped from company sustainability reports.

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Profiling CCD Documents with LEAD North’s CCD Data Profiler
Ever opened a CCD and been greeted by a wall of tangled XML? You’re not alone. Despite being a core format for clinical data exchange, CCD's are notoriously dense, verbose, and unfriendly to the human eye. For developers and analysts trying to validate their structure or extract meaningful insights, navigating these documents can feel more like archaeology than engineering.

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Like many others probably find themselves, we were stuck doing live data mapping in our Interface Engine that we really didn't want to do, but had no good alternative choice. We want to only keep mappings for as long as possibly needed and then purge expired rows based upon a TTL value. We actually had 4 use cases for it ourselves before we built this. Use cases:

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