Developing a novel causal inference algorithm for personalized biomedical causal graph learning using meta machine learning.
Journal:
BMC medical informatics and decision making
PMID:
38802809
Abstract
BACKGROUND: Modeling causality through graphs, referred to as causal graph learning, offers an appropriate description of the dynamics of causality. The majority of current machine learning models in clinical decision support systems only predict associations between variables, whereas causal graph learning models causality dynamics through graphs. However, building personalized causal graphs for each individual is challenging due to the limited amount of data available for each patient.