compbiomed-unito/survhive
Convenient, opinionated wrapper around some (deep) survival models
This tool helps researchers and analysts in fields like medicine or economics predict how long it will take for a specific event to occur, such as patient recovery or equipment failure. You provide data on past events and their timings, and it outputs predictions and analyses about future event durations. It's designed for data scientists and statisticians working with time-to-event data.
Use this if you need to build, compare, and fine-tune various survival analysis models, including advanced deep learning methods, to predict event timings.
Not ideal if you need a simple, off-the-shelf prediction without diving into model selection or advanced statistical techniques.
Stars
16
Forks
—
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Oct 20, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/compbiomed-unito/survhive"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Compare
Higher-rated alternatives
sebp/scikit-survival
Survival analysis built on top of scikit-learn
havakv/pycox
Survival analysis with PyTorch
Novartis/torchsurv
Deep survival analysis made easy
soda-inria/hazardous
Competing Risks and Survival Analysis
square/pysurvival
Open source package for Survival Analysis modeling