Labeling Self-Tracked Menstrual Health Records With Hidden Semi-Markov Models

Symul, Laura; Holmes, Susan

doi:10.1109/jbhi.2021.3110716

Cited by 2 publications

(3 citation statements)

References 49 publications

(56 reference statements)

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“…Menstrual cycles were identified from bleeding flows reported daily by participants on a scale from 0 (none) to 3 (heavy). A hidden semi-Markov model was specified to account for empirically observed distributions of cycle length and bleeding patterns across the menstrual cycle, including spotting between menses [ 46 ]. Data of participants who reported too few days with bleeding (i.e.…”

Section: Methodsmentioning

confidence: 99%

Sub-communities of the vaginal microbiota in pregnant and non-pregnant women

Symul,

Jeganathan,

Costello

et al. 2023

Proc. R. Soc. B.

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Diverse and non- Lactobacillus -dominated vaginal microbial communities are associated with adverse health outcomes such as preterm birth and the acquisition of sexually transmitted infections. Despite the importance of recognizing and understanding the key risk-associated features of these communities, their heterogeneous structure and properties remain ill-defined. Clustering approaches are commonly used to characterize vaginal communities, but they lack sensitivity and robustness in resolving substructures and revealing transitions between potential sub-communities. Here, we address this need with an approach based on mixed membership topic models. Using longitudinal data from cohorts of pregnant and non-pregnant study participants, we show that topic models more accurately describe sample composition, longitudinal changes, and better predict the loss of Lactobacillus dominance. We identify several non- Lactobacillus -dominated sub-communities common to both cohorts and independent of reproductive status. In non-pregnant individuals, we find that the menstrual cycle modulates transitions between and within sub-communities, as well as the concentrations of half of the cytokines and 18% of metabolites. Overall, our analyses based on mixed membership models reveal substructures of vaginal ecosystems which may have important clinical and biological associations.

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Section: Methodsmentioning

confidence: 99%

Sub-communities of the vaginal microbiota in pregnant and non-pregnant women

Symul,

Jeganathan,

Costello

et al. 2023

Proc. R. Soc. B.

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View full text Add to dashboard Cite

show abstract

“…Menstrual cycles were identified from bleeding flows reported by participants on a scale from 0 (none) to 3 (heavy). Specifically, a hidden semi-Markov model was specified to account for empirically observed distribution of cycle length and bleeding patterns across the menstrual cycle (67). Data of participants who reported too few days with bleeding (i.e., less than 3/70 study days) or too many (i.e., more than 30/70 study days) were excluded from the menstrual cycle analyses.…”

Section: Identification Of Phases Of the Menstrual Cyclementioning

confidence: 99%

“…These missing data points were imputed using the same KNN imputation method as for the metabolite KNN imputation. the menstrual cycle, including spotting between menses (55). Data of participants who reported too few days with bleeding (i.e., less than 3/70 study days) or too many (i.e., more than 30/70 study days) were excluded from the menstrual cycle analyses.…”

Section: Cytokine Concentration Quantificationmentioning

confidence: 99%

Sub-communities of the vaginal microbiota in pregnant and non-pregnant women

Symul

Jeganathan

Costello

et al. 2021

Preprint

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Diverse and non-Lactobacillus-dominated vaginal microbial communities are associated with adverse health outcomes such as preterm birth and acquisition of sexually transmitted infections. Despite the importance of recognizing and understanding the key risk-associated features of these communities, their heterogeneous structure and properties remain ill-defined. Clustering approaches have been commonly used to characterize vaginal communities, but they lack sensitivity and robustness in resolving community substructures and revealing transitions between potential sub-communities. We used a more highly resolved approach based on mixed membership topic models with multi-domain longitudinal data from cohorts of pregnant and non-pregnant subjects to identify several non-Lactobacillus-dominated sub-communities common to women regardless of reproductive status. These sub-communities correlated with clusters of metabolites. In non-pregnant subjects, we identified a few sub-communities that were more common during menses but did not predict an increased likelihood of non-Lactobacillus-dominated communities during the rest of the menstrual cycle. The menstrual cycle was a strong driver of transitions between sub-communities and was correlated with changes in levels of cytokines, for example, elevated TNF-α concentrations at the time of ovulation, and metabolites, for example, elevated kynurenine concentrations during menses. In pregnant women, some metabolite clusters were predictive of changes in vaginal microbiota structure. Overall, our results show that the vaginal community substructure is shaped by the menstrual cycle and that specific sets of metabolites are associated with community instability during pregnancy.

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Labeling Self-Tracked Menstrual Health Records With Hidden Semi-Markov Models

Cited by 2 publications

References 49 publications

Sub-communities of the vaginal microbiota in pregnant and non-pregnant women

Sub-communities of the vaginal microbiota in pregnant and non-pregnant women

Sub-communities of the vaginal microbiota in pregnant and non-pregnant women

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