Temporal rule induction for clinical outcome analysis

  • Authors:

  • Xiaohua Hu;Il-Yeol Song;Hyoil Han;Illhoi Yoo;Ann A. Prestrud;Murray F. Brennan;Ari D. Brooks

  • Affiliations:

  • College of Information Science and Technology, Drexel University, Philadelphia, PA 19104, USA.;College of Information Science and Technology, Drexel University, Philadelphia, PA 19104, USA.;College of Information Science and Technology, Drexel University, Philadelphia, PA 19104, USA.;College of Information Science and Technology, Drexel University, Philadelphia, PA 19104, USA.;Department of Surgery, College of Medicine, Drexel University, Philadelphia, PA 19102, USA.;Department of Surgery, Memorial Sloan-Kettering Cancer Center, New York, NY 10021, USA.;College of Medicine, Department of Surgery, Drexel University, Philadelphia, PA 19102, USA

  • Venue:
  • International Journal of Business Intelligence and Data Mining
  • Year:
  • 2005

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Abstract

Clinical outcomes analysis normally covers a particular time period. The sample under study is constantly changing as patients are censored, leave the study or die. In this paper, we present a novel data mining approach to mine temporal rules that reflect characteristics of outcomes analysis. We apply our temporal rule induction algorithm to a set of cancer patients, clinical records that were prospectively collected for 20 years. We analyse clinical data not only based on the static event, such as local recurrence for survival analysis, but also based on the temporal event with censored data for each time unit. The rules extracted from our temporal rule induction algorithm are compared to results from statistical analysis. The importance of this paper is that this novel temporal rule induction algorithm provides valuable insights for clinical data assessment and complements traditional statistical analysis.