Introduction
Python has become an increasingly vital tool for analyzing healthcare data. It is a widely used programming language. According to the PYPL (Popularity of Programming Language) index, it ranks as the world’s most popular programming language, commanding a 30.7% market share. By comparison, Java holds 14.89% and JavaScript 7.78% of the market.
Python’s success stems from its power, versatility, and user-friendly design. With its clear, readable syntax and gentler learning curve compared to other languages, Python is accessible to many users.
Furthermore, an active developer community has created extensive libraries and frameworks that enhance Python’s capabilities and ease of use.
With powerful libraries like TensorFlow, Keras, and Scikit-learn, Python has become the preferred language for machine learning and artificial intelligence development.
When properly implemented following best practices, these Python libraries can analyze healthcare data to enhance patient diagnosis and treatment outcomes.
In this article, we will briefly explore how to use Python to analyze healthcare data, covering the entire process from data import to results visualization.
Essential Python Libraries for Healthcare Data Management
Numpy and Pandas
These are are two essential Python libraries for data analysis, each with complementary functionalities particularly useful in healthcare.
NumPy provides the mathematical foundation for scientific computing in Python through high-performance multidimensional arrays and numerous mathematical functions that enable efficient complex calculations. This library allows for biomedical signal processing, diagnostic image analysis, and supports advanced statistical algorithms necessary for interpreting clinical data.
Pandas, on the other hand, focuses on structured data manipulation and analysis through its main data structures, DataFrame and Series, which greatly facilitate working with tabular information. In healthcare, Pandas excels in managing electronic health records, epidemiological data, and time series of clinical parameters, offering robust functionality for data cleaning, handling missing values, and information aggregation.
These two libraries are typically used in combination: NumPy provides the computational power necessary for underlying mathematical operations, while Pandas offers an intuitive interface to manipulate and explore healthcare datasets, enabling researchers and industry professionals to extract meaningful information, identify trends in patient data, and develop predictive models to improve diagnosis and treatments.
Pyhealth
Pyhealth is a specialized library for developing machine learning applications in healthcare. It supports major medical databases like MIMIC-III, MIMIC-IV, and eICU, providing base outputs for MIMIC-III. The library includes templates for key predictions such as readmission risk, length of stay, and treatment recommendations. It enables users to build predictive models and evaluate their performance. The library also supports over 20 medical coding systems, including ICD-9 and ICD-10, for diagnoses, treatments, and medications.
Lifelines
Lifelines is a tool for survival analysis using various techniques, including Kaplan-Meier, Nelson-Aalen, and regression. It covers most parametric and non-parametric methods and supports the creation of related graphs. Lifelines features an intuitive design and a scikit-learn-like API, making it easily accessible to data scientists and researchers who are already familiar with Python’s ecosystem.
Biopython
BioPython is a powerful tool for analyzing molecular and computational biology. The library streamlines common bioinformatics tasks, enabling researchers to concentrate on interpreting results instead of managing data.
Nilearn
Nilearn is a Python library for neuroimaging analysis and visualization built on scikit-learn. It is an essential tool for neuroscientists and researchers working with neuroimaging data, especially functional magnetic resonance imaging (fMRI).
By connecting traditional neuroimaging analysis with machine learning, Nilearn makes advanced statistical techniques more approachable for neuroscientists. Its comprehensive documentation, complete with tutorials and examples, ensures accessibility even for newcomers to the field.
The library integrates with Python’s scientific ecosystem—including NumPy, SciPy, Matplotlib, and scikit-learn—enabling efficient workflows in neuroscientific research.
Pymedtermino
It is a useful library for managing medical terminology. It supports various standards like ICD-10 and is beneficial for coding and analyzing healthcare data.
Pymc
Pymc is a package for running models based on Bayesian statistics, ideal for building healthcare models like predicting outcomes.
Libraries based on FHIR
FHIR (Fast Healthcare Interoperability Resources) is a standard developed by HL7 (Health Level Seven) for exchanging healthcare information between various systems and devices. Several Python packages are available for working with FHIR: Fhir.resources – Google-fhir-py – Fhirpack
Libraries for Medical Image Visualization
A crucial aspect of healthcare applications is managing and visualizing medical images. Python libraries are numerous and vital for creating these visual applications:
Matplotlib
Though not specifically designed for image visualization, Matplotlib excels in creating and displaying 2D and 3D graphs and images.
ITK
ITK is a tool that enables multidimensional image analysis and segmentation, especially for CT or MRI images. It also allows the alignment of images from various sources. SimpleITK, built on ITK, offers numerous image manipulation tools. These tools are powerful and widely used.
Medpy
Medpy is a collection of scripts that lets you manipulate, read, and write medical images in Python. Based on SimpleITK, Medpy supports numerous formats, from DICOM to those of the Neuroimaging Informatics Technology Initiative, Nrrd, MINC, GIPL, microscopic images, PNG, JPG, JPEG, TIFF, BMP, and more. It also enables feature extraction for use in machine learning programs like Scikit-Learn.
Scikit-image
It’s a collection of algorithms for image processing.
Pydicom
Pydicom is a Python library for working with DICOM files—reading, manipulating, and saving them. As a native Python application, it is easy for users to utilize.
To use these libraries in Python, you need to first install them on your system and then import them into your code. We recommend installing in a virtual environment, as shown in other articles on this blog.
Typically, installation is done by typing in the terminal, in pip environment:
pip install namelibraryIn Conda environment:
conda install -c conda-forge namelibraryGenerally, libraries installed with pip and those installed in a Conda environment are separate and not automatically accessible to each other. This difference arises because pip and Conda manage environments and dependencies differently. If you use both environments, it is advisable to perform both installations.
Some libraries need specific commands for installation. You can find detailed instructions on their respective linked Pyp pages.
After the installation is complete, you can import the library into your projects using the import statement:
import namelibrary
## or, if use with alias
import namelibrary as alias