This document mainly enriches the content of the previous article and introduces the use of the application.
Perhaps you have already read the previous article, but I still want to say, After completing the initialization operation (including model creation and training), the Fhir HepatitisC Predict application then predicts HepatitisC
In a world where healthcare technology is rapidly evolving, the importance of efficient, reliable, and interoperable healthcare applications has never been greater.
According Wikipedia a mind map is a diagram used to visually organize information into a hierarchy, showing relationships among pieces of the whole. It is often created around a single concept, drawn as an image in the center of a blank page, to which associated representations of ideas such as images, words and parts of words are added. Major ideas are connected directly to the central concept, and other ideas branch out from those major ideas.
An App that converts HL7 messages to JSON objects. About a year ago I started a GitHub repo for collecting stuff related to HL7. Recently my team added an HL7 interface to our Interoperability Production and we were asked to persist HL7 messages. We created a Kafka topic to receive HL7 messages. We use Kafka Bridges to send messages to Kafka topics. Kafka messages are sent to the Kafka Bridge in JSON format.
Django, a high-level web framework written in Python, has become a staple for developers seeking a robust, efficient, and easy-to-learn solution for building web applications. Its popularity stems from its versatility, offering developers an efficient toolkit for building web applications. Integrating Django with InterSystems IRIS introduces a dynamic synergy, providing developers with a comprehensive web development and database management solution. That's why on the Ideas Portal, @Evgeny Shvarov suggested that having Examples to work with IRIS from Django would be beneficial. In this article, we'll explore two projects created to answer the posted idea — Django-iris by @Dmitry Maslennikovand Iris-size-django by @Heloisa Paiva.
When I first encountered FHIR, I encountered a problem with its message format. It was difficult for me to determine whether the message I created met the format, and it was also difficult to easily create an FHIR message from scratch.
So, through fhir server of IntereSystems’fhirserver API, I created this application for quickly generating/validating FHIR messages.
I created this application considering how to convert images such as prescription forms into FHIR messages
It recognizes the text in the image through OCR technology and extracts it, which is then transformed into fhir messages through AI (LLA language model).
Finally, sending the message to the fhir server of IntereSystems can verify whether the message meets the fhir requirements. If approved, it can be viewed on the select page.
In 2021, I participated as an InterSystems mentor in a hackathon, where a newcomer to FHIR asked me if there was a tool to transform generic JSON data containing basic patient information into FHIR format. I informed her that I didn't know anything like that, unfortunately.
But that idea stays in my mind...
Several months later, in 2022, I came up with an idea to experiment: to train a named entity recognition (NER) to identify FHIR elements into generic texts. The training involved synthetic FHIR data generated by Synthea and the spaCy Python library.
We have a yummy dataset with recipes written by multiple Reddit users, however most of the information is free text as the title or description of a post. Let's find out how we can very easily load the dataset, extract some features and analyze it using features from OpenAI large language model within Embedded Python and the Langchain framework.
The %CSP.Login class is the utility class provided by InterSystems IRIS to do custom login pages. If you want to control your IRIS application authentication UI, you must extend %CSP.Login and override some methods according to your needs. This article is going to detail those methods and what you can do with them. In addition to that, you will get an explanation of the delegated authentication mechanism provided by ZAUTHENTICATE.mac routine.
🔥 Curious about the FHIR standard that everyone's talking about?
📚 Read on for a brief introduction, then try a brand-new learning path to get more in-depth knowledge!
The HL7® FHIR® standard has revolutionized the way healthcare developers take on the challenges of data interoperability. FHIR allows healthcare systems to exchange information seamlessly, and patient data can be consolidated in real time, regardless of where it's stored.
Processing FHIR resources with FHIR SQL BUILDER to predict the probability of developing hepatitis C disease
With the development of technology, the medical industry is also constantly advancing, and humans often pay more attention to their own health, By learning and processing datasets through computers, diseases can be predicted.
Pre condition: Ability to use FHIR and ML Firstly, our dataset is obtained from kaggle and transformed into FHIR resources based on patient gender, age, ALP or ALT, and imported into the FHIR resource repository
Considering new business interest in applying Generative-AI to local commercially sensitive private data and information, without exposure to public clouds. Like a match needs the energy of striking to ignite, the Tech lead new "activation energy" challenge is to reveal how investing in GPU hardware could support novel competitive capabilities. The capability can reveal the use-cases that provide new value and savings.
Sharpening this axe begins with a functional protocol for running LLMs on a local laptop.
Recently I was impressed by @Dan Pasco's article where he shares also how he uses terminal aliases.
Terminal aliases is a very powerful tool for developers and sys admins if you often need to call some cumbersome terminal expressions and make it shorter and cleaner. Here is the documentation. Yes!
But what about Docker environments? What if you are fan of Docker dev environments but also want to keep using your favorite aliases with Docker as well?
The ideal number of table permissions to assign for your users is zero. Permissions should be granted upon sign-in based on the application used for access. For web applications, we have a simple way of doing this by appointing application roles, matching roles, and required resources in the System Management Portal.
ODBC and JDBC connections present a different problem, however, especially when third-party applications are involved. As providers of an ERP system, our customers often wish to be able to employ various software packages to integrate with or report on their data. Many of these programs are capable of running any kind of query. Yet, letting them do that can be devastating to a customer’s data.
The traditional use of an IRIS production is for an inbound adapter to receive input from an external source, send that input to an IRIS service, then have that service send that input through the production.
All FHIR resources have a Meta element containing metadata about the resource. Some attributes are updated by the server, others are populated by the app constructing the resource.
IRIS can use a KMS (Key Managment Service) as of release 2023.3. Intersystems documentation is a good resource on KMS implementation but does not go into details of the KMS set up on the system, nor provide an easily followable example of how one might set this up for basic testing.
The purpose of this article is to supplement the docs with a brief explanation of KMS, an example of its use in IRIS, and notes for setup of a testing system on AWS EC2 RedHat Linux system using the AWS KMS. It is assumed in this document that the reader/implementor already has access/knowledge to set up an AWS EC2 Linux system running IRIS (2023.3 or later), and that they have proper authority to access the AWS KMS and AWS IAM (for creating roles and polices), or that they will be able to get this access either on their own or via their organizations Security contact in charge of their AWS access.
Did you know that you can get JSON data directly from your SQL tables?
Let me introduce you to 2 useful SQL functions that are used to retrieve JSON data from SQL queries - JSON_ARRAY and JSON_OBJECT. You can use those functions in the SELECT statement with other types of select items, and they can be specified in other locations where an SQL function can be used, such as in a WHERE clause
The JSON_ARRAY function takes a comma-separated list of expressions and returns a JSON array containing those values.
I have been struggling with a docker run command that kept crashing, the error message was too generic to point me to the right direction.
Since the container is shut down after the failure, I was unable to login to it in order to figure out the problem.
I had to run the container in a way that I'll be able to log into it before it crashed, so I found the adding -u false prevents the docker run command to run the iris session IRIS and the container stayed up and running. then I was able to log into it using:
Abstract Set Query, ASQ, is a super set of JSON Path Language, JPL, which was first introduced by the 2016 ISO SQL Standard. JPL is only defined within the context of SQL. In 2023.3, we introduced classes that implement ASQ. It works on any implementation of %Library.AbstractSet. The complete syntax for ASQ will be documented later. This is just a quick introduction to a few of its features. This implementation comes with a nice utility as well. You will know it when you see it!
If you need to migrate your server for some reason, you can reduce the setup work by copying configuration information from the pre-migration environment to the post-migration environment.
The following setting information can be migrated.
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