Natural Language Processing and soft data for motor skill assessment: A case study in surgical training simulations.

Journal: Computer methods and programs in biomedicine
PMID: 40081199

Abstract

BACKGROUND AND OBJECTIVE: Automated surgical skill assessment using kinematic and video data (hard data) sources has been widely adopted in the literature. However, experts' opinions (soft data) in the form of free-text could be an invaluable source for evaluating one's skill level since the availability and semantic richness of the soft data are both higher than the hard data. In this paper, the feasibility of using soft data as a single source of skill assessment is analyzed with various Natural Language Processing (NLP) algorithms of different levels of complexity.

Authors

  • Arash Iranfar
    School of Electrical and Computer Engineering, University College of Engineering, University of Tehran, N. Kargar st., Tehran, Iran. Electronic address: arash.iranfar@ut.ac.ir.
  • Mohammad Soleymannejad
    School of Electrical and Computer Engineering, University College of Engineering, University of Tehran, N. Kargar st., Tehran, Iran. Electronic address: soleymannejad@ut.ac.ir.
  • Behzad Moshiri
    School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran; Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, Canada.
  • Hamid D Taghirad
    Advanced Robotics and Automated Systems (ARAS), Faculty of Electrical Engineering, K.N. Toosi University of Technology, 470 Mirdamad Ave. West, 1 97, Tehran, Iran. Electronic address: taghirad@kntu.ac.ir.

Keywords

External Resources

Popular Topics
Recent Journals