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Python

Python is a high-level, interpreted programming language known for its simplicity and readability. Created by Guido van Rossum in 1991, Python has become one of the most popular programming languages worldwide. Its design philosophy emphasizes code readability with the use of significant whitespace and a clean, pragmatic syntax.

Why Python for the NHS?​

Python is an excellent choice for the NHS due to several factors:

  • Ease of learning: Python's simple syntax makes it accessible to beginners and experts alike. The NHS Python Community emphasizes this aspect, stating that Python's "accessible 'human readable' syntax" makes it "relatively easy to pick up and learn"1. This is crucial for healthcare professionals who may not have extensive programming backgrounds but need to work with data.
  • Versatility: Python can be used for data analysis, web development, machine learning, and more. The NHS Python Community highlights that Python is "used by developers, data scientists, and data engineers"1, showcasing its wide range of applications in healthcare settings.
  • Large community and extensive libraries: This ensures robust support and a wide range of tools for healthcare applications. The NHS Python Community itself is evidence of this, with its active presence on LinkedIn2, Slack channel, and YouTube channel. These platforms provide support and resources for NHS staff using Python.
  • Data processing capabilities: Python excels at handling large datasets, crucial for healthcare informatics. The NHS-R community, which works alongside the Python community, emphasizes the importance of data analysis tools in healthcare3, indicating Python's significance in this area.
  • Integration: It can easily integrate with existing systems and databases used in the NHS.

The NHS Python Community also highlights additional applications of Python in healthcare, which align with these factors:

  • Predictive analytics in patient care
  • Medical image analysis
  • Epidemiological research
  • Automating routine data processing tasks

Getting Started with Python​

To begin using Python, you'll need to set it up on your computer:

Installation​

  1. Download Python from the official Python website.
  2. Install Python by following the installation instructions for your operating system. Make sure to check the option to "Add Python to PATH" during installation. This allows you to run Python from the command line.

Verifying Installation​

After installing Python, it’s important to verify the installation:

  • Open a command prompt or terminal.
  • Type python --version and press Enter. You should see the version of Python you installed.

Setting Up a Virtual Environment​

Using a virtual environment for Python projects helps manage dependencies effectively, ensuring that each project has its own separate set of packages and libraries.

  1. Install virtualenv if not already installed:

    pip install virtualenv

  2. Create a new virtual environment

    virtualenv myenv

  3. Activate the virtual environment

    • On Windows
    myenv\Scripts\activate
    • On macOS and Linux
    source myenv/bin/activate

Writing and Running Your First Python Script​

Now that you have Python set up, you can write and run your first Python script.

  1. Open your preferred text editor or IDE (Integrated Development Environment) and create a new file named hello.py.

  2. In hello.py, write the following code:

     print("Hello, world!")

  3. Save the file and run it from the command line or terminal by navigating to the file's directory and typing:

     python hello.py

You should see the output: Hello, world!

Installing and Using Packages​

Python’s extensive library of packages can be easily accessed using pip, the package installer for Python.

  1. To install a package, i.e. the 'requests' library, you would use:

     pip install requests

  2. In your Python script, you can now use the installed package:

     import requests

     response = requests.get('https://api.github.com')
     print(response.status_code)

Basic Python Concepts​

Some fundamental Python concepts include:

Variables and data types​

Python supports various data types, including integers, floats, strings, and booleans. Variables are created by assigning a value to a name45. For example:

x = 5  # integer
y = 3.14  # float
name = "Python"  # string
is_true = True  # boolean

Python is dynamically typed, meaning you don't need to declare the type of a variable explicitly4.

Control structures (if statements, loops)​

Control structures allow you to control the flow of your program6.

  • If statements:
if x > 0:
    print("Positive")
elif x < 0:
    print("Negative")
else:
    print("Zero")
  • Loops:
for i in range(5):
    print(i)

while x > 0:
    print(x)
    x -= 1

Functions and modules​

Functions are reusable blocks of code6. Modules are files containing Python definitions and statements, which can be imported and used in other Python scripts7.

def greet(name):
    return f"Hello, {name}!"

import math
print(math.pi)

Object-oriented programming​

Python supports object-oriented programming (OOP) with classes and objects6. For example:

class Dog:
    def __init__(self, name):
        self.name = name
    
    def bark(self):
        print(f"{self.name} says Woof!")

my_dog = Dog("Buddy")
my_dog.bark()

Error handling​

Python uses try-except blocks for error handling6:

try:
    result = 10 / 0
except ZeroDivisionError:
    print("Cannot divide by zero")

These concepts form the foundation of Python programming. The Python documentation provides comprehensive information on all these topics, and many online resources offer tutorials and examples for learning and practicing these concepts4.

Python Libraries for Healthcare​

Several Python libraries are particularly useful for NHS data analysts and healthcare professionals:

NumPy and Pandas​

NumPy and Pandas are fundamental libraries for data manipulation and analysis in Python, widely used in healthcare data science.

  • NumPy provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays efficiently.
  • Pandas offers data structures like DataFrames, which are particularly useful for handling structured data such as patient records or clinical trial results.

For example, Pandas can be used to analyze patient data, calculate statistics, and perform time-series analysis on health metrics8. A study published in the Journal of Medical Internet Research used Pandas for data preprocessing in a machine learning model for predicting hospital readmissions9.

Matplotlib and Seaborn​

These libraries are essential for creating informative visualizations of healthcare data.

  • Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python.
  • Seaborn, built on top of Matplotlib, provides a high-level interface for drawing attractive statistical graphics.

Researchers have used these libraries to visualize trends in patient data, create dashboards for hospital management, and present results of clinical studies8.

Scikit-learn​

Scikit-learn is a machine learning library that provides simple and efficient tools for data mining and data analysis. In healthcare, it's used for tasks such as:

  • Predicting patient outcomes
  • Classifying diseases based on symptoms or test results
  • Clustering patients for personalized treatment plans

A study in Nature Scientific Reports used Scikit-learn to develop a machine learning model for predicting the risk of chronic kidney disease progression10.

NLTK​

The Natural Language Toolkit (NLTK) is used for working with human language data, which is particularly useful in healthcare for:

  • Analyzing clinical notes and medical records
  • Extracting information from medical literature
  • Processing patient feedback and surveys

Researchers have used NLTK to extract medical concepts from clinical text and to analyze sentiment in patient reviews of healthcare providers.

Biopython​

Biopython is a set of tools for biological computation, including:

  • Reading and writing various sequence file formats
  • Accessing online biological databases
  • Performing sequence analysis

It's particularly useful in genomics research and bioinformatics applications in healthcare. For instance, it has been used in studies analyzing genetic sequences related to diseases11.

Examples of Python in Action​

Below are some examples of Python in use within the NHS:

  • ESNEFT Analysis - Diabetes Inequalities: This project, developed by Stephen Richer, focuses on analyzing inequalities in population health data, specifically for diabetes. It involves data manipulation and uses libraries like OSNnx.
  • DNAttend - ML framework for predicting patient non-attendance: Another project by Stephen Richer, this uses machine learning (specifically CatBoost) to predict patient non-attendance, which is crucial for resource management in healthcare settings.
  • Discrete Event Simulation - Ambulances: Developed by Martina Fonseca, this project uses the simmer discrete event simulation library in R to model ambulance services. While not in Python, it demonstrates the application of programming in healthcare logistics.
  • Using LIME to explain facial disease classification: Anwesha Mohanty's project applies Local Interpretable Model-agnostic Explanations (LIME) to an InceptionV3 Classifier for facial disease classification, specifically looking at Rosacea. This project showcases the use of explainable AI in medical diagnostics.
  • AIF ALlocation Streamlit Tool: Created by Craig Shenton & Christopher Todd, this tool supports need-based allocations in healthcare. It's built using Streamlit and Python, demonstrating the use of these technologies for healthcare resource management.
  • GP Mapping: Mattia Ficarelli's project tracks the number of patients registered at individual GP Practices, using Python for data analysis and visualization in primary care settings.
  • The Prescribing Cost of Antibiotics: Another project by Mattia Ficarelli, this uses Plotly and Folium (Python libraries) to visualize the prescribing cost of antibiotics like Amoxicillin and Doxycycline Hyclate.

Footnotes​

  1. Anonymous. "NHS Python Community" NHS PyCom, Link ↩ ↩2

  2. Anonymous. "NHS Python Community for Healthcare" LinkedIn, Link ↩

  3. Transformation Directorate. "Network and engagement" NHS England, Link ↩

  4. Waseem, Mohammad. "What Are Python Data Types and Variables?" edureka!, 2024, Link ↩ ↩2 ↩3

  5. Anonymous. "Python Data Types" Programiz, Link ↩

  6. Ramos, P. Leodanis. "Basic Data Types in Python: A Quick Exploration" Real Python, 2024, Link ↩ ↩2 ↩3 ↩4

  7. Metcalfe, Paige. "Variables & Data Types – Python" University of Salford, Link ↩

  8. Nagamalaiyan, Jayapandian. "Python in Healthcare" LinkedIn, 2023, Link ↩ ↩2

  9. Llego, A. Mark. "Healthcare Data Analysis with Pandas in Python" llego.dev, Link ↩

  10. Anonymous. "Python in Healthcare: AI Applications in Hospitals" Datacamp, 2022, Link ↩

  11. Lee, Doris. "Secure and Scalable Healthcare Analytics in Python with Snowflake" Python Health Data Science, 2023, Link ↩