nriss / ImplementationGuide_OsirisFHIR

Home Page:https://nriss.github.io/ImplementationGuide_OsirisFHIR/master/ig

Geek Repo:Geek Repo

Github PK Tool:Github PK Tool

IG-OSIRIS

By Arkhn, data for open and collaborative healthcare

IG-OSIRIS is the implementation guide of the data framework OSIRIS for clinical and biological data sharing and interoperability in oncology.

Context

What is an IG?

FHIR Implementation Guides (IGs) are a set of rules of how a particular interoperability or standards problem is solved.

To define these rules, IGs include:

  • Computable rules
  • Human-readable versions of the computable rules
  • Additional narrative descriptions of the rules, and additional information about the problem the IG is trying to solve

The high-level goal of an IG is to provide sufficiently detailed instructions to implementers so that they can independently set up systems that will be able to successfully communicate with each other.

To meet this goal, the computable rules are often insufficient. IGs usually include a significant amount of narrative content to supplement the computable rules, which may describe:

  • The primary use cases that are in the scope of the IG
  • The actors, and how the actors are expected to interact
  • How the components of the IG can be used together to meet the needs of these actors for these use cases
  • Conformance criteria that are not able to be represented in the computable portion of the IG

What is OSIRIS?

OSIRIS is a minimum data set for data sharing and interoperability in oncology, developed by Integrated Cancer Research Sites (SIRICs), in the context of large precision medicine clinical trials to incorporate the longitudinal changes associated with disease progression and resistance to therapeutic interventions. It could also provide an effective real-world data ecosystem by developing a data standard, which, if used, could improve the compatibility, quality, and consistency of electronic health record. In both cases, OSIRIS may facilitate the application of artificial intelligence and enhance supervised machine learning and data science in the context of clinical care and clinical research.

Publication Minimum Data Set for Data Sharing and Interoperability in Oncology
OSIRIS Data Framework github.com/siric-osiris/OSIRIS

Getting started

Recommended

Install VS Code

  1. Install Visual Studio Code IDE
brew install visual-studio-code
  1. In VS Code, install the extension Dev Containers

  2. Install Docker

Run

To start you work session:

  1. Open Docker
  2. Open VS Code
  3. Clone this repository on your computer
git clone https://github.com/arkhn/arkhn-ig-osiris.git
  1. Run the Dev Containers: Open Folder in Container... command (⇧⌘P) and select the local folder.

Manual installation

If you want to do a manual installation, follow this: Manual Installation Guide

Generate IG

  • Run SUSHI
sushi .

Running SUSHI will create a arkhn-ig-osiris/fsh-generated directory, and populate it with the files needed to create the IG using the HL7 FHIR IG Publisher tool.

The files generated by SUSHI are located in the arkhn-ig-osiris/fsh-generated directory. Check that the arkhn-ig-osiris/fsh-generated/resources subdirectory contains two generated StructureDefinitions and a generated ImplementationGuide.

  • Update FHIR IG publisher

macOs:

./_updatepublisher.sh

Windows:

_updatepublisher.sh

This will download the latest version of the HL7 FHIR IG Publisher tool into ./input-cache. This step can be skipped if you already have run the command recently, and have the latest version of the IG Publisher tool.

  • Run the HL7 FHIR IG generator:

macOs:

./_genonce.sh

Windows:

_genonce.sh

After the publisher is finished, open the file arkhn-ig-osiris/output/index.html to see the resulting IG.

About

https://nriss.github.io/ImplementationGuide_OsirisFHIR/master/ig

License:GNU General Public License v3.0


Languages

Language:HTML 99.8%Language:GLSL 0.2%Language:CSS 0.0%Language:Shell 0.0%Language:Batchfile 0.0%Language:JavaScript 0.0%Language:Dockerfile 0.0%