🏥 NeMo Data Designer: Clinical Trials Dataset Generator

📚 What you'll learn

This notebook demonstrates how to use structured samplers, person/PII generators, and LLMs to create a realistic
synthetic clinical trials dataset—including trial metadata, participant demographics, investigator details,
clinical notes, and adverse event reports—for evaluating data protection and anonymization techniques.

👋 IMPORTANT – Environment Setup

• If you haven't already, follow the instructions in the README to install the necessary dependencies.

• You may need to restart your notebook's kernel after setting up the environment.

• In this notebook, we assume you have a self-hosted instance of Data Designer up and running.

• For deployment instructions, see the Installation Options section of the NeMo Data Designer documentation.

📦 Import the essentials

• The data_designer module of nemo_microservices exposes Data Designer's high-level SDK.

• The essentials module provides quick access to the most commonly used objects.

⚙️ Initialize the NeMo Data Designer Client

• NeMoDataDesignerClient is responsible for submitting generation requests to the microservice.

🎛️ Define model configurations

• Each ModelConfig defines a model that can be used during the generation process.

• The "model alias" is used to reference the model in the Data Designer config (as we will see below).

• The "model provider" is the external service that hosts the model (see the model config docs for more details).

• By default, the microservice uses build.nvidia.com as the model provider.

🏗️ Initialize the Data Designer Config Builder

• The Data Designer config defines the dataset schema and generation process.

• The config builder provides an intuitive interface for building this configuration.

• The list of model configs is provided to the builder at initialization.

🎲 Getting Started with Sampler Columns

• Sampler columns offer non-LLM based generation of synthetic data.

• They are particularly useful for steering the diversity of the generated data, as we demonstrate below.

• The persona samplers allow you to sample realistic details of individuals using a model trained on the US Census.
  If the locale of the persona you are generating is anything other than en_US, then the personas will be generated using Faker

Creating Trial Information

Next, we'll create the basic trial information:

• Study ID (unique identifier)
• Trial phase and therapeutic area
• Study design details
• Start and end dates for the trial

Participant Information

Now we'll create fields for participant demographics and enrollment details:

• Participant ID and basic information
• Demographics (age, gender, etc.)
• Enrollment status and dates
• Randomization assignment

Investigator and Staff Information

Here we'll add information about the trial staff:

• Investigator information (principal investigator)
• Study coordinator details
• Site information

Clinical Measurements and Outcomes

These columns will track the key clinical data collected during the trial:

• Vital signs and lab values
• Efficacy measurements
• Dosing information

Adverse Events Tracking

Here we'll capture adverse events that occur during the clinical trial:

• Adverse event presence and type
• Severity and relatedness to treatment
• Dates and resolution

Narrative text fields with style variations

These fields will contain natural language text that incorporates PII elements. We'll use style seed categories to ensure diversity in the writing styles:

1. Medical observations and notes
2. Adverse event descriptions
3. Protocol deviation explanations

Note: At this time, we only support using a single file as the seed. If you have multiple files you would like to use as seeds, it is recommended you consolidated these into a single file.

Adding Constraints

Finally, we'll add constraints to ensure our data is logically consistent:

• Trial dates must be in proper sequence
• Adverse event dates must occur after enrollment
• Measurement changes must be realistic

🔁 Iteration is key – preview the dataset!

1. Use the preview method to generate a sample of records quickly.

2. Inspect the results for quality and format issues.

3. Adjust column configurations, prompts, or parameters as needed.

4. Re-run the preview until satisfied.

📊 Analyze the generated data

• Data Designer automatically generates a basic statistical analysis of the generated data.

• This analysis is available via the analysis property of generation result objects.

🆙 Scale up!

• Happy with your preview data?

• Use the create method to submit larger Data Designer generation jobs.