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.
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Radovanovic, S., Vukicevic, M., Kovacevic, A., Stiglic, G., Obradovic, Z. (2015). Domain knowledge Based Hierarchical Feature Selection for 30-Day Hospital Readmission Prediction. In: Holmes, J., Bellazzi, R., Sacchi, L., Peek, N. (eds) Artificial Intelligence in Medicine. AIME 2015. Lecture Notes in Computer Science(), vol 9105. Springer, Cham. https://doi.org/10.1007/978-3-319-19551-3_11
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DOI: https://doi.org/10.1007/978-3-319-19551-3_11
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-19550-6
Online ISBN: 978-3-319-19551-3
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