Association between maternal overprotection and premenstrual disorder
A machine learning based exploratory study
Bibliographic Data
| ID | 19536735 |
|---|---|
| Authors | Kaori Tsuyuki (Kyoto University), Miho Egawa (0000-0001-5848-1001, Kyoto University), Takuma Ohsuga (0000-0001-7382-3313, Kyoto University), Akihiko Ueda (0000-0001-8139-9292, Kyoto University), Kazuki Shimada (0000-0002-9850-5095, Kyoto University Hospital), Tsukasa Ueno (Kyoto University), Kazuko Hiyoshi (Taisei Gakuin University), Keita Ueda (0000-0002-4626-3477, Kyoto Koka Women's University), Masaki Mandai (0000-0003-4428-8029, Kyoto University) |
| Year | 2025 |
| Volume | 19 |
| Issue | 1 |
| Pages | 4-4 |
| Publication date | 2025-02-24 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | BioPsychoSocial Medicine (JOURNAL) |
| Journal identifiers | ISSN: 1751-0759 • E-ISSN: 1751-0759 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1186/s13030-025-00326-y |
| PMID | 39994774 |
| OpenAlex | W4407885096 |
| Language | EN |
| References cited | 41 |
BACKGROUND: Premenstrual disorder (PMD), which includes premenstrual syndrome and premenstrual dysphoric disorder, has a complex pathogenesis and may be closely related to emotional cognition and memory. However, the mechanisms underlying these associations remain unclear. Therefore, this study used machine learning to explore the roles of various factors that are not typically considered risk-factors for PMD. METHODS: A predictive model for PMD was constructed using a dataset of questionnaire responses and heartrate variability data collected from 60 participants during their follicular and luteal phases. Based on the Japanese version of the Premenstrual Symptom Screening Tool, the binary objective variable (PMD status) was defined as "PMD" for moderate-to-severe premenstrual syndrome and premenstrual dysphoric disorder and other conditions as "non-PMD." The contribution of each feature to the predictive model was assessed using the Shapley Additive exPlanations (SHAP) model-interpretation framework. RESULTS: Of the 58 participants (providing 117 data points), 17 (34 data points) were in the PMD group and 41 (83 data points) were in the non-PMD group. The area under the receiver operating characteristic curve was 0.90 (95% confidence interval: 0.82-0.98). Among the top 20 features with the highest SHAP values, six were associated with maternal bonding. Four of the six mother-related characteristics were associated with overprotection. CONCLUSIONS: Based on these findings, parental bonding experiences, including maternal overprotection, may be associated with the presence of PMD
Confidence interval · Developmental psychology · Follicular phase · Hormone · Luteal phase · Machine learning · Menstrual cycle · Premenstrual dysphoric disorder · Psychotherapist · Receiver operating characteristic · Structural equation modeling · Clinical Psychology · Maternal Mental Health During Pregnancy and Postpartum · Medicine · Menstrual Health and Disorders · Neuroendocrine regulation and behavior · Psychology · Internal Medicine
Diagnostic and Statistical Manual of Mental Disorders
Society and the Adolescent Self-Image
Development of a fatigue scale
Machine Learning in Medicine
From local explanations to global understanding with explainable AI for trees
Roles of childhood maltreatment, personality traits, and life stress in the prediction of severe premenstrual symptoms
The Development of a Menstrual Distress Questionnaire
Validation of the State-Trait Distinction in Anxiety Research
A Parental Bonding Instrument
Individual differences in two emotion regulation processes
| Highly cited | No |
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