Bayesian Network structure learning algorithm for highly missing and non imputable data: Application to breast cancer radiotherapy data.

Fiche publication

Date publication

janvier 2024

Journal

Artificial intelligence in medicine

Auteurs

Membres identifiés du Cancéropôle Est :
Dr CLAVIER Jean-Baptiste , Dr GUIHARD Sébastien

Tous les auteurs :
Piot M, Bertrand F, Guihard S, Clavier JB, Maumy M

Lien Pubmed

https://www.ncbi.nlm.nih.gov/pubmed/38184350

Résumé

It is not uncommon for real-life data produced in healthcare to have a higher proportion of missing data than in other scopes. To take into account these missing data, imputation is not always desired by healthcare experts, and complete case analysis can lead to a significant loss of data. The algorithm proposed here, allows the learning of Bayesian Network graphs when both imputation and complete case analysis are not possible. The learning process is based on a set of local bootstrap learnings performed on complete sub-datasets which are then aggregated and locally optimized. This learning method presents competitive results compared to other structure learning algorithms, whatever the mechanism of missing data.