Recently I noticed a Kaggle dataset for the prediction of whether a Covid-19 patient will be admitted to ICU. It is a spreadsheet of 1925 encounter records of 231 columns of vital signs and observations, with the last column of "ICU" being 1 for Yes or 0 for No. The task is to predict whether a patient will be admitted to ICU based on known data.
The fhir-react project defines a unique component which renders the interface based on FHIR resource type. There's no need for any configuration because the library uses the standard of defined by the resource type.
So, I decided to apply the same idea, but for charts. The basic idea is depicted below:
It design it's really friendly - there's just one component! As FHIR resource types are standards, the framework resolves internally what rendering class must be used.
To display your FHIR resource just write this component:
We are looking to better understand how our users configure and manage our products. If you have a few minutes, please fill out this quick survey https://www.surveymonkey.com/r/N2JX3TQ
If you're willing to participate in an in-depth interview about your experiences, you might be eligible for a $100 gift card! Indicate in the survey that you'd like to talk to us and we will be in touch the second week in September!
I am able to get a service class property DISPLAYLIST into the SETTINGS PARAMETER using the below example from the documention but I am not able to get an adapter property in the same manner. Is there a way to obtain the DISPLAYLIST values from an adapter property into the SETTINGS Parameter?
for reference I am extending the adapter: EnsLib.HL7.Adapter.TCPInboundAdapter
As I said in the previous article, I started to learn about FHIR for the contest, and I'd like to share an update in my application: detection of inconsistencies in FHIR data.
In my previous articles, one of the most exciting things about FHIR that I mentioned it's the wide range of possibilities that we have and not only for creating something but the ways to achieve this goal.
Nowadays, there is a lot of applications that are using Open Authorization framework (OAuth) to access resources from all kinds of services in a secure, reliable and efficient manner. InterSystems IRIS is already compatible with OAuth 2.0 framework, in fact, there is a great article in the community regarding OAuth 2.0 and InterSystems IRIS in the following link here.
NLP stands for Natural Language Processing which is a field of Artificial Intelligence with a lot of complexity and
techniques to in short words "understand what are you talking about".
And FHIR is...???
FHIR stands for Fast Healthcare Interoperability Resources and is a standard to data structures for healthcare. There are
some good articles here explainig better how FHIR interact with Intersystems IRIS.
I want to share my experience creating the iris-fhir-portal with FHIR.
I decided to take a step forward and join the IRIS for Health FHIR contest, but I had never worked with FHIR before.
After the FHIR Contest Kick-Off Webinar, where we got an overview of how the IRIS for Health works with FHIR, I started to looking at the FHIR documentation to create my Patient Chart project.
Recently, I get interest in FHIR in order to run for the IRIS for Health FHIR
contest. As a beginner on this topic, I've heard somewhat about it, but I didn't know how complex and powerful was FHIR. As pointed out by @Henrique.GonçalvesDias here, you can model several aspects of the patient history and other related entities.
https://www.youtube.com/embed/GBo9rhRPWO8 [This is an embedded link, but you cannot view embedded content directly on the site because you have declined the cookies necessary to access it. To view embedded content, you would need to accept all cookies in your Cookies Settings]
Hi Community, As you may know, on Global Masters you can redeem a consultation with InterSystems expert on any InterSystems product: InterSystems IRIS, IRIS for Health, Interoperability (Ensemble), IRIS Analytics (DeepSee), Caché, HealthShare.
And we have exciting news for you: now these consultations available in the following languages: English, Portuguese, Russian, German, French, Italian, Spanish, Japanese, Chinese.Also! The duration is extended to 1.5 hours for your deep dive into the topic.
Continued from the previous Part I ... In part I, we walked through traditional ML approaches on this Covid-19 dataset on Kaggle.
In this Part II, let's run the same data & task, in its simplest possible form, through IRIS integratedML which is a nice & sleek SQL interface for backend AutoML options. It uses the same environment.