Mining Compact Predictive Pattern Sets Using Classification Model

  • Matteo MantovaniEmail author
  • Carlo Combi
  • Milos Hauskrecht
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11526)


In this paper, we develop a new framework for mining predictive patterns that aims to describe compactly the condition (or class) of interest. Our framework relies on a classification model that considers and combines various predictive pattern candidates and selects only those that are important for improving the overall class prediction performance. We test our approach on data derived from MIMIC-III EHR database, focusing on patterns predictive of sepsis. We show that using our classification approach we can achieve a significant reduction in the number of extracted patterns compared to the state-of-the-art methods based on minimum predictive pattern mining approach, while preserving the overall classification accuracy of the model.



This work was supported by NIH grant R01-GM088224. The content of this paper is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Matteo Mantovani
    • 1
    Email author
  • Carlo Combi
    • 1
  • Milos Hauskrecht
    • 2
  1. 1.Department of Computer ScienceUniversity of VeronaVeronaItaly
  2. 2.Department of Computer ScienceUniversity of PittsburghPittsburghUSA

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