A Risk Estimation System to Predict Postpartum Cigarette Smoking Relapse

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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)
Title of host publicationProceedings of the 2021 Annual Modeling and Simulation Conference, ANNSIM 2021
EditorsCristina Ruiz Martin, Maria Julia Blas, Alonso Inostrosa Psijas
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781565553750
DOIs
StatePublished - Jul 19 2021
Event2021 Annual Modeling and Simulation Conference, ANNSIM 2021 - Virtual, Fairfax, United States
Duration: Jul 19 2021Jul 22 2021

Publication series

NameProceedings of the 2021 Annual Modeling and Simulation Conference, ANNSIM 2021

Conference

Conference2021 Annual Modeling and Simulation Conference, ANNSIM 2021
Country/TerritoryUnited States
CityVirtual, Fairfax
Period7/19/217/22/21

Bibliographical note

Publisher Copyright:
© 2021 SCS.

Keywords

  • cigarette smoking
  • ecologically momentary assessment
  • hierarchical fuzzy inference system
  • particle swarm optimization
  • postpartum relapse prevention

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