Identifying Symptom Clusters in Women Experiencing Preterm Birth

Ting He, Casey Overby

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

Abstract

Preterm birth affected about 10% infants born in the U.S. in 2016. This project was a secondary analysis of data drawn from a preterm birth prediction study to assess whether psychological symptom clusters exist among pregnant women. A symptom cluster exists when two or more symptoms co-occur, are related to each other, and are stable. We found one psychological symptom pair that satisfied these conditions: anxiety & self-esteem. This finding has potential to help guide symptom assessment among pregnant women.

Original languageEnglish (US)
Title of host publicationACM-BCB 2018 - Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics
PublisherAssociation for Computing Machinery, Inc
Number of pages1
ISBN (Electronic)9781450357944
DOIs
StatePublished - Aug 15 2018
Event9th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, ACM-BCB 2018 - Washington, United States
Duration: Aug 29 2018Sep 1 2018

Other

Other9th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, ACM-BCB 2018
CountryUnited States
CityWashington
Period8/29/189/1/18

Fingerprint

Premature Birth
Pregnant Women
Psychology
Symptom Assessment
Self Concept
Anxiety

Keywords

  • Correlation
  • Preterm birth
  • Symptom clusters

ASJC Scopus subject areas

  • Computer Science Applications
  • Software
  • Health Informatics
  • Biomedical Engineering

Cite this

He, T., & Overby, C. (2018). Identifying Symptom Clusters in Women Experiencing Preterm Birth. In ACM-BCB 2018 - Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics Association for Computing Machinery, Inc. https://doi.org/10.1145/3233547.3233629

Identifying Symptom Clusters in Women Experiencing Preterm Birth. / He, Ting; Overby, Casey.

ACM-BCB 2018 - Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics. Association for Computing Machinery, Inc, 2018.

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

He, T & Overby, C 2018, Identifying Symptom Clusters in Women Experiencing Preterm Birth. in ACM-BCB 2018 - Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics. Association for Computing Machinery, Inc, 9th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, ACM-BCB 2018, Washington, United States, 8/29/18. https://doi.org/10.1145/3233547.3233629
He T, Overby C. Identifying Symptom Clusters in Women Experiencing Preterm Birth. In ACM-BCB 2018 - Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics. Association for Computing Machinery, Inc. 2018 https://doi.org/10.1145/3233547.3233629
He, Ting ; Overby, Casey. / Identifying Symptom Clusters in Women Experiencing Preterm Birth. ACM-BCB 2018 - Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics. Association for Computing Machinery, Inc, 2018.
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