This repository contains a modular wrapper of the "Heterogeneous Incomplete Variational Autoencoder model (HI-VAE)". The previous implementation branch (v1) has been used in:
Karwath et al., Redefining β-blocker response in heart failure patients with sinus rhythm and atrial fibrillation: a machine learning cluster analysis, Lancet, 2021 paper.
This modular wrapper was written by Andreas Karwath (a.karwath@bham.ac.uk) and Fathy Shalaby.
The original coding was done by Alfredo Nazabal (anazabal@turing.ac.uk) et al. written in Python and details can be found in this paper. The original code can be found here: https://github.com/probabilistic-learning/HI-VAE
This is an extenstion of implementations as easy to use Python library, upgraded for tensorflow2.
Please cite both papers if you should use this code/library for your own research.
See examples directory for usage
- For questions regarding algorithm --> Alfredo Nazabal: anazabal@turing.ac.uk
- For bugs or suggestion regarding this code --> Andreas Karwath: a.karwath@bham.ac.uk
This version requires tf2 (please not because of issues with installing tf2 on an Apple silicon, this is not specified in the setup.py as a requirement). For apple silcone users, please follow : https://developer.apple.com/metal/tensorflow-plugin/