Domain knowledge Based Hierarchical Feature Selection for 30-Day Hospital Readmission Prediction

Abstract

Many studies fail to provide models for 30-day hospital re-admission prediction with satisfactory performance due to high dimensionality and sparsity. Efficient feature selection techniques allow better generalization of predictive models and improved interpretability, which is a very important property for applications in health care. We propose feature selection method that exploits hierarchical domain knowledge together with data. The new method is evaluated on predicting 30-day hospital readmission for pediatric patients from California and provides evidence that a knowledge-based approach outperforms traditional methods and that the newly proposed method is competitive with state-of-the-art methods.

Sandro Radovanović

Assistant Professor at University of Belgrade

My research interests include machine learning, development and design of decision support systems, decision theory, and fairness and justice concepts in algorithmic decision making.