andreazignoli/pyoxynet
The Oxynet Python package repository
This project offers tools for automatically interpreting cardiopulmonary exercise test (CPET) data. It takes raw or pre-processed CPET measurements like oxygen uptake and exhaled CO2, and uses AI models to estimate exercise intensity domains or generate synthetic CPET data for research and training. Cardiac and pulmonary specialists, exercise physiologists, and clinical researchers would find this valuable for streamlining diagnostics and data analysis.
Use this if you need to quickly interpret complex CPET data to understand a patient's exercise capacity or generate realistic synthetic CPET datasets for studies.
Not ideal if you prefer manual, expert-driven interpretation of CPET data without the aid of automated machine learning models.
Stars
17
Forks
4
Language
Python
License
MIT
Category
Last pushed
Oct 21, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/andreazignoli/pyoxynet"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
pykt-team/pykt-toolkit
pyKT: A Python Library to Benchmark Deep Learning based Knowledge Tracing Models
microsoft/archai
Accelerate your Neural Architecture Search (NAS) through fast, reproducible and modular research.
google-research/morph-net
Fast & Simple Resource-Constrained Learning of Deep Network Structure
AI-team-UoA/pyJedAI
An open-source library that leverages Python’s data science ecosystem to build powerful...
IDEALLab/EngiBench
Benchmarks for automated engineering design