Inductive database to support iterative data mining: Application to biomarker analysis on patient data in the Fight-HF project

• DOI: 10.1016/j.jbi.2022.104212
• Publication Date: 2022-09-28T16:44:59Z
• Authors: Emmanuel Bresso; Joao-Pedro Ferreira; Nicolas Girerd; Masatake Kobayashi; Grégoire Preud’homme; Patrick Rossignol; Fayez Zannad; Marie-Dominique D...; Malika Smaïl-Tabbone
• Keywords: Heart Failure; Databases, Factual; Knowledge Discovery; 3. Good health; 03 medical and health sciences; 0302 clinical medicine; [SDV.MHEP.CSC]Life Sciences [q-bio]/Human health and pathology/Cardiology and cardiovascular system; Data Mining; Inductive database; Knowledge Discovery from Data (KDD); Data mining; Biomarkers

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).