An innovative artificial intelligence-based method to compress complex models into explainable, model-agnostic and reduced decision support systems with application to healthcare (NEAR).

Journal: Artificial intelligence in medicine
Published Date: Mar 12, 2024

Abstract

BACKGROUND AND OBJECTIVE: In everyday clinical practice, medical decision is currently based on clinical guidelines which are often static and rigid, and do not account for population variability, while individualized, patient-oriented decision and/or treatment are the paradigm change necessary to enter into the era of precision medicine. Most of the limitations of a guideline-based system could be overcome through the adoption of Clinical Decision Support Systems (CDSSs) based on Artificial Intelligence (AI) algorithms. However, the black-box nature of AI algorithms has hampered a large adoption of AI-based CDSSs in clinical practice. In this study, an innovative AI-based method to compress AI-based prediction models into explainable, model-agnostic, and reduced decision support systems (NEAR) with application to healthcare is presented and validated.

Authors

  • Karim Kassem
    Polito(BIO)Med Lab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Turin, Italy.
  • Michela Sperti
    PolitoBIOMed Lab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Torino, Italy.
  • Andrea Cavallo
    Department of Psychology, University of Torino, Via Po, 14, 10123, Torino, Italy. andrea.cavallo@unito.it.
  • Andrea Mario Vergani
    Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Via Ponzio 34/5, 20133 Milan, Italy; Department of Mathematics, Politecnico di Milano, Via Bonardi 9, 20133 Milan, Italy; Health Data Science Centre, Human Technopole, Viale Rita Levi-Montalcini 1, 20157 Milan, Italy.
  • Davide Fassino
    Department of Mathematical Sciences, Politecnico di Torino, Turin, Italy.
  • Monica Moz
    Fondazione Bruno Kessler Research Institute, Trento, Italy.
  • Alessandro Liscio
    Dedalus Research Lab, Milan, Italy.
  • Riccardo Banali
    Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy.
  • Michael Dahlweid
    Dedalus Research Lab, Milan, Italy.
  • Luciano Benetti
    Dedalus Research Lab, Milan, Italy.
  • Francesco Bruno
    Division of Cardiology, Cardiovascular and Thoracic Department, Molinette Hospital, Città della Salute e della Scienza, Turin, Italy.
  • Guglielmo Gallone
    Division of Cardiology, Cardiovascular and Thoracic Department, Città della Salute e della Scienza, Turin, Italy; Cardiology, Department of Medical Sciences, University of Turin, Turin, Italy.
  • Ovidio De Filippo
    Division of Cardiology, Cardiovascular and Thoracic Department, Città della Salute e della Scienza, Turin, Italy; Cardiology, Department of Medical Sciences, University of Turin, Turin, Italy.
  • Mario Iannaccone
    Department of Cardiology, S G Bosco Hospital, Turin, Italy.
  • Fabrizio D'Ascenzo
    Division of Cardiology, Cardiovascular and Thoracic Department, Città della Salute e della Scienza, Turin, Italy; Cardiology, Department of Medical Sciences, University of Turin, Turin, Italy. Electronic address: fabrizio.dascenzo@gmail.com.
  • Gaetano Maria De Ferrari
    Coronary Care Unit, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy.
  • Umberto Morbiducci
    Polito BIO Med Lab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Turin, Italy.
  • Emanuele Della Valle
    Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Via Ponzio 34/5, 20133 Milan, Italy.
  • Marco Agostino Deriu
    Polito BIO Med Lab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Turin, Italy.

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