Classifying smoking urges via machine learning.

Journal: Computer methods and programs in biomedicine
Published Date: Sep 23, 2016

Abstract

BACKGROUND AND OBJECTIVE: Smoking is the largest preventable cause of death and diseases in the developed world, and advances in modern electronics and machine learning can help us deliver real-time intervention to smokers in novel ways. In this paper, we examine different machine learning approaches to use situational features associated with having or not having urges to smoke during a quit attempt in order to accurately classify high-urge states.

Authors

  • Antoine Dumortier
    Department of Electrical and Computer Engineering, University of Pittsburgh, Benedum Hall, Pittsburgh, PA 15260, USA.
  • Ellen Beckjord
    Department of Psychiatry, University of Pittsburgh, 5115 Centre Avenue, Suite 140, Pittsburgh, PA 15232, USA.
  • Saul Shiffman
    Department of Psychology, University of Pittsburgh, 510 BELPB, 130 N. Bellefield Avenue, Pittsburgh, PA 15260, USA.
  • Ervin Sejdić
    Department of Electrical and Computer Engineering, University of Pittsburgh, Benedum Hall, Pittsburgh, PA 15260, USA. Electronic address: esejdic@ieee.org.

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