Supported Libraries

Health Universe is designed to be compatible with a wide range of technologies and libraries, enabling developers to leverage the best tools and frameworks for their healthcare applications. This page provides an overview of the key technologies and libraries supported by the Health Universe platform, grouped by their primary functions.

Data Management and Storage

To manage and store healthcare data, Health Universe supports several popular Python libraries and storage solutions including but not limited to:

  • Pandas: A powerful data manipulation and analysis library for handling structured data in DataFrames.

  • NumPy: A library for working with numerical data in multi-dimensional arrays, offering a range of mathematical and statistical functions.

  • Requests: A library for making HTTP requests to RESTful APIs and handling API responses.

  • Streamlit.experimental_connection:

    • st.experimental_connection() factory method to initialize ready-to-use data connection objects

    • Concrete implementations built into Streamlit for SQL and Snowpark

    • Easy to install connections for cloud file storage and Google Sheets

  • Amazon S3, Google Cloud Storage: Cloud storage services for storing and retrieving large volumes of data in a scalable and cost-effective manner.

Machine Learning and Artificial Intelligence

Health Universe supports a variety of Python libraries for developing machine learning and artificial intelligence models:

  • scikit-learn: A comprehensive library for machine learning, offering a wide range of algorithms for classification, regression, clustering, and dimensionality reduction.

  • TensorFlow: An open-source machine learning framework developed by Google, particularly suited for deep learning applications.

  • PyTorch: A flexible and efficient machine learning library developed by Facebook, with strong support for deep learning and dynamic computation graphs.

  • Keras: A high-level neural networks API, running on top of TensorFlow, that simplifies the process of building and training deep learning models.

  • XGBoost: An optimized distributed gradient boosting library, designed for high performance and scalability in machine learning tasks.

Data Visualization and User Interface

Health Universe leverages Streamlit for building interactive web applications and supports various data visualization libraries:

  • Streamlit: An open-source app framework for creating data-driven web applications in Python, offering a simple API for building custom user interfaces with minimal effort.

  • Plotly: A powerful data visualization library that allows developers to create interactive and responsive charts and graphs for web applications.

  • Matplotlib: A versatile plotting library for creating static, animated, and interactive visualizations in Python.

  • Seaborn: A statistical data visualization library based on Matplotlib, offering high-level interfaces for drawing informative and attractive visualizations.

Collaboration and Deployment

Health Universe supports tools and technologies for application collaboration, version control, and deployment:

  • Git: A distributed version control system for tracking changes in source code and managing collaboration among developers.

  • GitHub: Cloud-based platforms for hosting Git repositories and facilitating collaboration, code review, and project management.

  • Docker: A platform for developing, shipping, and running applications in containers, ensuring consistent and reproducible deployment across environments.

  • Kubernetes, Amazon ECS, Azure Kubernetes Service: Container orchestration platforms for deploying, scaling, and managing containerized applications in cloud environments.

By supporting a wide range of open-source technologies and libraries, Health Universe empowers developers to choose the best tools for their healthcare applications, fostering innovation and collaboration in the field of health research and care.

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