patGAN

patGAN is a Python tool that allows you to generate synthetic clinical study data in the form of individual synthetic patients. The data is generated using a GAN (Generative Adversarial Network) that was trained on a subset of the Framingham Heart Study data. The individual patients consist of continuous and binary demographic and clinical features, including vital parameter measurements, lifestyle choices, and medical history. The generated data is exported to a CSV file.

Requirements

Before using patGAN, make sure your environment meets the following requirements:

• Python 3.x (The tool was tested with Python 3.11)
• Required dependencies (See installation guide below)

Installation

1. Clone or download this repository to your local machine.

2. Navigate to the root directory of the project.

3. Install the required dependencies from the requirements.txt file:

   pip install -r requirements.txt

How to Use

To use patGAN, follow these steps:

1. Make sure you have the required dependencies installed.

2. Open a terminal or command prompt in the root directory of your project.

3. Run the CLI by executing the following command: python patgan.py [OPTIONS]

4. You can get an overview of all available options by displaying the help section: python patgan.py -h

CLI Options

The patGAN CLI supports the following options:

• -n or --n_patients: Specifies the number of patients to generate. The default value is 100.
• -o or --output: Specifies the name of the output CSV file. The default name is generated_patients.csv

Examples

Here are some example usages of the patGAN CLI:

1. Generate the default amount of patients (100):

   python patgan.py
   

2. Generate a specific amount of patients (e.g. 1000):

   python patgan.py -n 1000
   

3. Generate the default amount of patients (100) and specify the output CSV name:

   python patgan.py -o custom_name
   

4. Generate a specific amount of patients (e.g. 5000) and specify the output CSV name:

   python patgan.py -n 5000 -o my_patients
   

Patient Features

Name	Description	Type
age	Age of the patient (years)	Continuous
totChol	Total cholesterol level of the patient (mg/dL)	Continuous
sysBP	Systolic blood pressure of the patient (mmHg)	Continuous
diaBP	Diastolic blood pressure of the patient (mmHg)	Continuous
BMI	Body Mass Index of the patient (kg/m²)	Continuous
heartRate	Heart Rate of the patient (bpm)	Continuous
glucose	Glucose level of the patient (mg/dL)	Continuous
cigsPerDay	Number of cigarettes smoked per day	Discrete Count
male	Biological sex of the patient is male	Binary
smoker	Patient is an active smoker	Binary
BPMeds	Patient takes blood pressure medication	Binary
prevStroke	Patient has a history of stroke	Binary
hypertension	Patient suffers from hypertension	Binary
diabetes	Patient has diabetes	Binary