Skip to content

more corrections to the documentation #847

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
jhnwu3 merged 2 commits into from
Feb 13, 2026
Merged

more corrections to the documentation #847

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
7332a28
more corrections to the documentation
jhnwu3 Feb 13, 2026
01cef58
more clean up
jhnwu3 Feb 13, 2026
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
2 changes: 1 addition & 1 deletion docs/index.rst
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -147,7 +147,7 @@ Quick Navigation

- **Research Initiative**

Join our summer research program and contribute
Join our year round research program and contribute

:doc:`View Projects → <research_initiative>`

Expand Down
45 changes: 10 additions & 35 deletions docs/why_pyhealth.rst
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -11,33 +11,6 @@ PyHealth is the comprehensive Python library for healthcare AI that makes buildi
.. note::
📄 **Read the PyHealth 2.0 paper**: `PyHealth 2.0: A Comprehensive Open-Source Toolkit for Accessible and Reproducible Clinical Deep Learning <https://arxiv.org/pdf/2601.16414>`_

Performance Benchmarks
=======================

PyHealth 2.0 delivers exceptional performance that makes healthcare AI research accessible on standard hardware:

**Breakthrough speed improvements:**

- **Up to 39× faster** task processing compared to typical pandas-based approaches
- Dramatically reduced processing time for common clinical prediction tasks
- Optimized data loaders with smart caching and lazy evaluation
- Efficient multi-core scaling without memory overflow

**Memory efficiency:**

- **Dynamically scales to fit consumer-grade hardware** (16GB laptops)
- Handles large-scale datasets like MIMIC-IV without requiring workstation-grade resources
- Intelligent memory management adapts to available system resources
- Enables research on complex healthcare datasets without expensive infrastructure

.. image:: ../figure/PyHealthPerformanceResults.drawio.png
:alt: PyHealth 2.0 performance benchmarks showing speed and memory efficiency
:align: center
:width: 700px

.. note::
**What this means for researchers:** PyHealth 2.0 enables you to run sophisticated healthcare AI analyses on a standard laptop that previously required high-end workstations. The platform adapts to your available resources while maintaining high performance.

----

What Makes PyHealth 2.0 Powerful?
Expand Down Expand Up @@ -331,13 +304,15 @@ Join our active healthcare AI community:
- **Support**: Active Discord community and GitHub discussions - `Join our Discord <https://discord.gg/mpb835EHaX>`_

Get Started Today
=================
===========

Ready to begin? Explore these key resources:

Ready to build your first healthcare AI application? See the resources below:
- :doc:`how_to_get_started` — Quickstart guide
- :doc:`install` — Install PyHealth
- :doc:`tutorials` — Interactive tutorials
- :doc:`api/models` — Model API docs
- :doc:`api/datasets` — Datasets
- :doc:`api/tasks` — Tasks

- :doc:`how_to_get_started` - Build your first model in minutes
- :doc:`install` - Detailed installation instructions
- :doc:`tutorials` - Interactive tutorials and examples
- :doc:`api/models` - Complete API documentation
- :doc:`api/datasets` - Available datasets
- :doc:`api/tasks` - Pre-defined tasks
Jump in with the guides above, or use the navigation on the left for more.