A risk estimation system to predict postpartum cigarette smoking relapse

Minsik Hong, Jerzy W. Rozenblit, Alicia Allen, Uma S. Nair, Sharon Allen

Research output: Contribution to journalConference articlepeer-review


Postpartum relapse to cigarette smoking (PRS) rate has not substantially improved for more than two decades. Over 55% of women successfully quit smoking during pregnancy; however, half (50%) return to smoking within three months of childbirth and 90% relapse within a year. The identification of effective PRS prevention interventions are needed, especially since factors related to PRS risk factors vary by person, time, and context. In this paper, a prototype risk estimation system using daily ecological momentary assessment data is proposed to develop an adaptive intervention system which will consider multiple risk factors. The risk estimator is designed using a hierarchical fuzzy inference system design scheme to capture human knowledge. A particle swarm optimization scheme is also applied. The simulation results show the feasibility of the proposed estimator for the PRS prevention intervention system.

Original languageEnglish (US)
Pages (from-to)192-202
Number of pages11
JournalSimulation Series
Issue number2
StatePublished - 2021
Event2021 Annual Modeling and Simulation Conference, ANNSIM 2021 - Virtual, Online
Duration: Jul 19 2021Jul 22 2021


  • Cigarette smoking
  • Ecologically momentary assessment
  • Hierarchical fuzzy inference system
  • Particle swarm optimization
  • Postpartum relapse prevention

ASJC Scopus subject areas

  • Computer Networks and Communications


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