2024-11-25-donker24a.md

title	abstract	openreview	layout	series	publisher	issn	id	month	tex_title	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Multinomial belief networks for healthcare data	Healthcare data from patient or population cohorts are often characterized by sparsity, high missingness and relatively small sample sizes. In addition, being able to quantify uncertainty is often important in a medical context. To address these analytical requirements we propose a deep generative Bayesian model for multinomial count data. We develop a collapsed Gibbs sampling procedure that takes advantage of a series of augmentation relations, inspired by the Zhou–Cong–Chen model. We visualise the model’s ability to identify coherent substructures in the data using a dataset of handwritten digits. We then apply it to a large experimental dataset of DNA mutations in cancer and show that we can identify biologically meaningful clusters of mutational signatures in a fully data-driven way.	043h6W3veQ	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	donker24a	0	Multinomial belief networks for healthcare data	false	Donker, Hylke Cornelis and Neijzen, Dorien and de Jong, Johann and Lunter, Gerton	given family Hylke Cornelis Donker given family Dorien Neijzen given family prefix Johann Jong de given family Gerton Lunter	2024-11-25		Proceedings of the 9th Machine Learning for Healthcare Conference	252	inproceedings	date-parts 2024 11 25	https://raw.githubusercontent.com/mlresearch/v252/main/assets/donker24a/donker24a.pdf