Cluster Analysis of Alcohol Consumption during Pregnancy in the Safe Passage Study

Nicolo Pini, Lauren C. Shuffrey, Maristella Lucchini, Ayesha Sania, Morgan E. Nelson, J. David Nugent, Timothy N. Ochoa, Hein J. Odendaal, William P. Fifer, Michael M. Myers, Amy J. Elliott

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

Abstract

Characterization of patterns of alcohol consumption during pregnancy encompasses multiple factors such as magnitude, frequency, and timing of exposure throughout gestation. Traditional statistical models are limited in dealing with multivariate and diverse patterns of exposure as in the context of this analysis. We propose a finite mixture model-based approach to derive clusters of alcohol exposure of participants in the Safe Passage Study (PASS). Daily alcohol consumption data for 11,083 pregnant women have been clustered in 10 different exposed groups. The resulting cluster analysis was able to characterize alcohol consumption in a comprehensive framework capable of taking into account both quantity and timing of exposure as well as the occurrence of binge drinking.

Original languageEnglish (US)
Title of host publication2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1338-1341
Number of pages4
ISBN (Electronic)9781538613115
DOIs
StatePublished - Jul 2019
Event41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019 - Berlin, Germany
Duration: Jul 23 2019Jul 27 2019

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)1557-170X

Conference

Conference41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
CountryGermany
CityBerlin
Period7/23/197/27/19

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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