workshop-integrated-csv

Example about an IRIS production using IntegratedML capabilities to get predictions about stays of patients in hospitals

You can find more in-depth information in https://learning.intersystems.com.

New to IRIS Interoperability framework? Have a look at IRIS Interoperability Intro Workshop.

What do you need to install?

Git
Docker (if you are using Windows, make sure you set your Docker installation to use “Linux containers”).
Docker Compose
Visual Studio Code + InterSystems ObjectScript VSCode Extension

Setup

Build the image we will use during the workshop:

$ git clone https://github.com/intersystems-ib/workshop-integratedml-csv
$ cd workshop-integratedml-csv
$ docker-compose build

Example

The main purpose of this example is to get predictions about the stay of a patient with a hip fracture in the hospital. We are going to use real data about episodes in hospitals of the Castilla y León HealthService (SACYL) from Spain. For this example we are going to use the Record Mapper functionality of IRIS to import these data from CSV files. The creation and the training of the model will be executed the specific SQL queries available in IntegratedML

Test Production

Run the containers we will use in the workshop:

docker-compose up -d

Automatically an IRIS instance will be deployed and a production will be configured and run available to import data to create the prediction model and train it.

Open the Management Portal.
Login using the default superuser/ SYS account.
Select Namespace MLTEST
Click on Test Production to access the sample production that we are going to use. You can access also through Interoperability > User > Configure > Production.
Click on CSVToEpisodeTrain Business Service and review the configuration, check the input folder. From Visual Studio Code copy train-data.csv and paste it into /shared/csv/trainIn
Do the same for CSVToEpisode Business Service, from Visual Studio Code copy test-data.csv and paste it into /shared/csv/newIn
Now you have the data ready for the model creation and training. Open the Business Operation PredictStayEpisode.cls from Visual Studio Code and review the queries used for the creation of the model and the training. As you can see, the model will be created the first time that the Business Operation is invoked from RecordToEpisodeBPL.cls .
To start the predictions you only have to copy messagesa01.hl7 file into /shared/hl7/in, the production will start to consume ADT^A01 messages and generating predictions for each record, you can check it from the Messages log.