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Let's Build: FHIR from Jupyter

In this lab, we import the following Jupyter notebooks into IBM Watson Studio within the IBM Cloud Pak for Data:

  1. The FHIR API
  2. FHIR in Spark
  3. Predictive Modeling

Notebook 1 is configured to use an instance of the IBM FHIR Server (running on IBM Cloud) which has been loaded with sample data generated from the Synthea™️ Patient Generator.

Notebook 2 requires access to a Cloud Object Store with FHIR R4 resources from SyntheticMass.

Notebook 3 builds a predictive model from Parquet files that are built in notebook 2 from features extracted from the FHIR resources.

Notebook 1 should work from almost any Jupyter notebook environment, but notebooks 2 and 3 use Apache Spark to process bulk FHIR resources into a dataframe and therefor must run in a Jupyter environment with access to Spark.

Getting started

Download the notebooks

Clone or download the notebooks from this repo to your local system. download the notebooks

Register / log in to IBM Cloud

  1. Navigate to https://dataplatform.cloud.ibm.com/registration/stepone
  2. Choose Dallas to be nearest to the data for this lab register for IBM Cloud

If you already have an IBMid, enter it on the right. If not, enter a valid email address on the left, agree to the terms, click Next, and check your email to complete the registration process.

Once registered and logged in, continue to the Watson Studio dashboard. the watson studio dashboard

Create a project

  1. From the IBM Cloud Pak for Data dashboard, click New project + new project dialog
  2. Select Create an empty project create a project
  3. Give the project a name (e.g. FHIR from Jupyter) and click Add to define a storage service create cloud object storage
  4. Select a plan (the free Lite plan should be fine) and click Create
  5. Click Refresh to see your Cloud Object Storage instance appear and then click Create

project dashboard

Create a notebook

From the Project dashboard, click Add to project + and choose the Notebook asset type. create a notebook

Select the From file tab and select the Default Spark 2.4 & Python 3.7 environment. Then, upload one of the notebook files that was downloaded in the Download the notebooks section and click Create.

Repeat this process for the other notebooks that you desire to load.