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

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.

• Type: Journal
• Year: 2015
• Where: AIME
• Authors: Sandro Radovanovic, Milan Vukicevic, Ana Kovacevic, Gregor Stiglic, Zoran Obradovic
• Keywords: AIME 2015, Artificial Intelligence