Keywords: Anaconda, Jupyter Notebook, Tensorflow GPU, Deep Learning, Python 3 and HealthShare

1. Purpose and Objectives

This "Part I" is a quick record on how to set up a "simple" but popular deep learning demo environment step-by-step with a Python 3 binding to a HealthShare 2017.2.1 instance . I used a Win10 laptop at hand, but the approach works the same on MacOS and Linux.

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I needed to pass through a file with Ensemble but the operation wasn't writing some filenames as given because the EnsLib.File.PassthroughOperation 'sanitizes' filenames removing characters that are not valid on some operating
systems;
09000655_AEDC_C3344059_A/E_Martin Browne_09000655_201706221018.pdf
09000655_AEDC_C3344059_A#47E_Martin_Browne_09000655_201706221018.pdf

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I already talked about GraphQL and the ways of using it in this article. Now I am going to tell you about the tasks I was facing and the results that I managed to achieve in the process of implementing GraphQL for InterSystems platforms.

What this article is about

  • Generation of an AST for a GraphQL request and its validation
  • Generation of documentation
  • Generation of a response in the JSON format
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Article
· Jun 16, 2023 10m read
Creating a REST service in IRIS

One of the most common needs of our clients is the creation of REST services that allow access to the information present in IRIS / HealthConnect. The advantage of these REST services is that it allows the development of custom user interfaces with the most current technologies taking advantage of the reliability and performance of IRIS in the back-end.

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Fun or No Fun - how serious is it?


Large language models are stirring up some phenomena in recent months. So inevitably I was playing ChatGPT too over last weekend, to probe whether it would be a complimentary to some BERT based "traditional" AI chatbots I was knocking up, or rather would it simply sweep them away.

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InterSystems and Intel recently conducted a series of benchmarks combining InterSystems IRIS with 2nd Generation Intel® Xeon® Scalable Processors, also known as “Cascade Lake”, and Intel® Optane™ DC Persistent Memory (DCPMM). The goals of these benchmarks are to demonstrate the performance and scalability capabilities of InterSystems IRIS with Intel’s latest server technologies in various workload settings and server configurations. Along with various benchmark results, three different use-cases of Intel DCPMM with InterSystems IRIS are provided in this report.

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Keywords: Jupyter Notebook, Tensorflow GPU, Keras, Deep Learning, MLP, and HealthShare

1. Purpose and Objectives

In previous"Part I" we have set up a deep learning demo environment. In this "Part II" we will test what we could do with it.

Many people at my age had started with the classic MLP (Multi-Layer Perceptron) model. It is intuitive hence conceptually easier to start with.

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Because I had no idea how to build an integration solution for HL7 and didn't know where to start, I decided to follow the course Building Basic HL7 Integrations with InterSystems on Learning portal to get at least the idea of where to begin. After I studied all of it, I decided it might be a good idea to share my thoughts and reflections about it with everyone.

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After upgrading an Information Exchange/HealthShare instance that has a registry namespace, you may notice that the Caché audit log gets filled with frequent login failures by the HS_Services user (check this by going to Management Portal > System Administration > Security > Auditing > View Audit Database). Clicking the “details” shows event data like:

Error message: Access Denied
CSP Application: /csp/healthshare/hsregistry
Authentication: Delegated
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Overview

We often run into connectivity problems with HealthShare (HS) deployments in Microsoft Azure that have multiple HealthShare components (instances or namespaces) installed on the same VM, especially when needing to communicate to other HS components while using the Azure Load Balancer (ILB) to provide mirror VIP functionality. Details on how and why a load balancer is used with database mirroring can be found this community article.

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Article
· Oct 24, 2022 10m read
PerfTools IO Test Suite

Purpose

This set of tools (RanRead, RanWrite, and the combined RanIO) is used to generate random read and write events within a database (or pair of databases) to test the IO speed of IRIS running on a specified hardware setup. While Read operations can be measured in the usual Input/Output operations per second (IOPS) since they're direct disk reads, write events are sent to the database and thus their physical writes are managed by IRIS's write daemon.

Results gathered from the IO tests will vary from configuration to configuration based on the IO sub-system. Before running these tests, ensure corresponding operating system and storage level monitoring are configured to capture IO performance metrics for later analysis. The suggested method is by running the System Performance tool that comes bundled within IRIS. Please note that this is an update to a previous release, which can be found here.

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1. Purpose

This is a 10-minute simple step-by-step guide on how to quickly set up various flavors of HealthShare docker containers from scratch on a Win10 laptop.

For example, we can build a couple of HealthShare "global edition vs UK Edition" demos as shown below.

There are a couple of frequently asked questions from HealthShare colleagues and partners:

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Artificial intelligence has solved countless human challenges – and medical coding might be next.
As organizations prepare for ICD-11, medical coding is about to become more complicated. Healthcare organizations in the United States already manage 140,000+ codes in ICD-10. With ICD-11, that number will rise.
Some propose artificial intelligence as a solution. AI could aid computer-based medical coding systems, identifying errors, enhancing patient care, and optimizing revenue cycles, among other benefits.

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Article
· Apr 25, 2023 12m read
Configuring Mirror in Docker

A common need for our customers is to configure both HealthShare HealthConnect and IRIS in high availability mode.

It's common for other integration engines on the market to be advertised as having "high availability" configurations, but that's not really true. In general, these solutions work with external databases and therefore, if these are not configured in high availability, when a database crash occurs or the connection to it is lost, the entire integration tool it becomes unusable.

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Article
· Jul 8, 2024 2m read
Configuring IRIS autostart on Linux

For practical reasons, it may be desirable that after a Linux server restart, the IRIS instance is automatically started.

Below you will find the steps to follow to automate the startup of IRIS during a reboot of the Linux server, via systemd :

1. Create an iris.service file in /etc/systemd/system/iris.service containing the following information

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Article
· Mar 24, 2016 3m read
HealthShare 2 Slack Business Operation

This will be a stretch to be useful, but it was sorta fun. If you just so happen to have a use case to make your HealthShare productions talk to a Slack channel, this is the Business Operation for you.

In Slack, it is a dead simple process to enable an incoming web hook:

  • Name It
  • Give it an Icon or Emoji
  • Declare a Channel to Interact With

One you supplies those, it spits out a URL that you can go to town posting to your channel using that endpoint.

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Hi Community,

This is my first post in the developer community - would appreciate any feedback!

For testing or demo purposes, you may want to send emails from your Interoperability Production. In this post then I will walk you through connecting an InterSystems IRIS Production to Gmail so you can use it to send emails alerts.

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Article
· May 15, 2020 3m read
Debugging in HealthShare

Hi, if you develop production with HealthShare, you may have noticed there is a nice tracing feature available - HS.Util.Trace.Operations. This feature allows visually tracing debug information, in structured way. It complements good old logging macros like $$$LOGINFO, $$$TRACE and alike.

I use this feature a lot. However, it has limited functionality, it works nice with classes that extend XML Adapter, streams or data types, but it doesn't work well with arbitrary objects that do no extend from XML Adapter.

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In the previous article we have reviewed how to install our EMPI in standalone, so we are ready to start the basic configuration of our EMPI.

First of all we have to do an initial basic configuration, we can access to the configuration from the Configuration menu of our Registry.

Selecting that option will allow to us to edit the basic configuration table of the Registry:

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Deploying InterSystems HealthShare code, supporting lookups and artifacts like ssl certs, keys etc is relatively straight forward using Gitlab Runners. Not only does this approach enable managing the code base and deploying with git type workflows, but it also lends to a speedy recovery and repeatable environments for some implementations.

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Article
· Nov 2, 2023 3m read
How to hide the source program

InterSystems FAQ rubric

For routines (*.mac)

You can hide the source by exporting/importing only the *.obj that is generated after compiling the source program.

The command execution example specifies EX1Sample.obj and EX2Sample.obj, which are generated by compiling EX1Sample.mac and EX2Sample.mac, as export targets and exports them to the second argument file.

After moving to another namespace, I am using the exported XML file to perform the import.

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Article
· Feb 14, 2017 1m read
Portal tip: The inconspicuous Menu button

Amongst the large fonts and chunky icons of Portal's pages, the Menu button in the top left corner is easily overlooked:

When clicked, it often produces the following menu:

When I remember it's there, I find the "View Console Log" option particularly handy.

I wrote "often" above because I've also noticed that the Menu contents change when I'm on a page within the Ensemble section of Portal:

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Article
· Nov 20, 2023 3m read
Parquet files and InterSystems IRIS

In the world of Big Data, selecting the right file format is crucial for efficient data storage, processing, and analysis. With the massive amount of data generated every day, choosing the appropriate format can greatly impact the speed, cost, and accuracy of data processing tasks. There are several file formats available, each with its own set of advantages and disadvantages, making the decision of which one to use complex. Some of the popular Big Data file formats include CSV, JSON, Avro, ORC, and Parquet.

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