Mining Surgery Phase-Related Sequential Rules from Vertebroplasty Simulations Traces

Conference paper

DOI: 10.1007/978-3-319-19551-3_5

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9105)
Cite this paper as:
Toussaint BM., Luengo V. (2015) Mining Surgery Phase-Related Sequential Rules from Vertebroplasty Simulations Traces. 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

Abstract

We present in this paper an algorithm for extracting perceptual-gestural rules from heterogeneous multisource traces. The challenge that we address is two-fold: 1) represent traces such that they render coherently all aspect of this multimodal knowledge; 2) ensure that key tutoring services can be produced on top of represented traces. In the spirit of automatic knowledge acquisition paradigm proposed in the literature, we implemented PhARules, a modified version of an existing algorithm, CMRules, for mining surgery phase-aware sequential rules from simulated surgery traces. We demonstrated the efficiency of our algorithm as well its performance limits on traces of simulations of vertebroplasty recorded in TELEOS, an Intelligent Tutoring System dedicated to percutaneous orthopedic surgery.

Keywords

Intelligent tutoring systems Educational data mining Sequential rules mining Simulated surgery Percutaneous orthopedic surgery 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Université Grenoble AlpesSt-Martin d’HèresFrance
  2. 2.Ecole Supérieure d’Infotronique d’HaïtiHaitiUSA

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