In Journal of biomedical informatics ; h5-index 55.0
Machine learning is now an essential part of any biomedical study but its integration into real effective Learning Health Systems, including the whole process of Knowledge Discovery from Data (KDD), is not yet realised. We propose an original extension of the KDD process model that involves an inductive database. We designed for the first time a generic model of Inductive Clinical DataBase (ICDB) aimed at hosting both patient data and learned models. We report experiments conducted on patient data in the frame of a project dedicated to fight heart failure. The results show how the ICDB approach allows to identify biomarker combinations, specific and predictive of heart fibrosis phenotype, that put forward hypotheses relative to underlying mechanisms. Two main scenarios were considered, a local-to-global KDD scenario and a trans-cohort alignment scenario. This promising proof of concept enables us to draw the contours of a next-generation Knowledge Discovery Environment (KDE).
Bresso Emmanuel, Ferreira Joao-Pedro, Girerd Nicolas, Kobayashi Masatake, Preud’homme Grógoire, Rossignol Patrick, Zannad Fayez, Devignes Marie-Dominique, Smaïl-Tabbone Malika
Biomarkers, Data mining, Heart Failure, Inductive database, Knowledge Discovery from Data (KDD)