A machine learning model to predict the risk of perinatal depression - Psychosocial and sleep-related factors in the Life-ON study cohort

Aug 5, 2026

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Perinatal depression (PND) is a frequent complication of pregnancy with serious consequences for mothers and infants. Early identification of women at elevated risk is essential. A machine learning framework was built using data from the multicenter Life-ON cohort, linking sociodemographic variables, blood-based biomarkers, sleep, medical and psychological measures from 439 pregnant women, and polysomnographic data from 353 women, to future depression assessed with the Edinburgh Postnatal Depression Scale across the perinatal period.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages9

Reading Time23 min

AccessFree to download

Introduction

Defining perinatal depression and clinical impact

Screening instruments and limitations of existing risk factors

Role of machine learning in perinatal risk analysis

Psychiatry Research 337 (2024) 115957 Contents lists available at ScienceDirect Psychiatry Research journal [homepage:](homepage: www.elsevier.com/locate/psychres)[ www.elsevier.com/locate/psychres](homepage: www.elsevier.com/locate/psychres) | A machine learning model to predict the risk of perinatal depression: Psychosocial and sleep-related factors in the Life-ON study cohort
Corrado Garbazzaa, b, c, 1, *, Francesca Mangilid, 1, Tatiana Adele D’Onofrio d, Daniele Malpettid, Silvia Riccardia, Alessandro Cicoline, Armando D’Agostino f, g, Fabio Cirignottah,
Mauro Manconia, i
a Sleep Medicine Unit, Neurocenter of Southern Switzerland, Lugano, Switzerland b Centre for Chronobiology, University of Basel, Basel, Switzerland
c Research Cluster Molecular and Cognitive Neurosciences, University of Basel, Basel, Switzerland d Istituto Dalle Molle di Studi sull’Intelligenza Artificiale (IDSIA), USI/SUPSI, Lugano, Switzerland e Sleep Medicine Center, Department of Neuroscience, University of Turin, Turin, Italy f Department of Mental Health and Addiction, ASST Santi Paolo e Carlo, Milan, Italy g Department of Health Sciences, Universit`a degli Studi di Milano, Milan, Italy
h University of Bologna, Bologna, Italy
i Faculty of Biomedical Sciences, Universit`a della Svizzera Italiana, Lugano, Switzerland
the “Life-ON” study group | | | --- | --- | | A R T I C L E I N F O
Keywords: Depression Sleep Women Pregnancy Postpartum Risk factors | A B S T R A C T | | | Perinatal depression (PND) is a common complication of pregnancy associated with serious health consequences for both mothers and their babies. Identifying risk factors for PND is key to early detect women at increased risk of developing this condition. We applied a machine learning (ML) approach to data from a multicenter cohort study on sleep and mood changes during the perinatal period (“Life-ON”) to derive models for PND risk prediction in a cross-validation setting. A wide range of sociodemographic variables, blood-based biomarkers, sleep, medical, and psychological data collected from 439 pregnant women, as well as polysomnographic parameters recorded from 353 women, were considered for model building. These covariates were correlated with the risk of future depression, as assessed by regularly administering the Edinburgh Postnatal Depression Scale across the perinatal period. The ML model indicated the mood status of pregnant women in the first trimester, previous depressive episodes and marital status, as the most important predictors of PND. Sleep quality, insomnia symptoms, age, previous miscarriages, and stressful life events also added to the model performance. Besides other predictors, sleep changes during early pregnancy should therefore assessed to identify women at higher risk of PND and support them with appropriate therapeutic strategies. | 1. Introduction Perinatal depression (PND) refers to the occurrence of a major depressive episode during pregnancy or within 4 weeks after childbirth, although most experts agree that any depressive episode up to one year postpartum should be considered as PND (Dagher et al., 2021). Common symptoms of PND include depressed mood and energy, weepiness, reduced appetite or overeating, either excessive or disrupted sleep, feelings of unworthiness and overworry about the well-being of the baby, and even thoughts of harming oneself or the baby (Van Niel and Payne, 2020). Due to its high prevalence rate (ca. 12 % of women affected worldwide) (Woody et al., 2017) and the detrimental impact on the health of mothers, children, and their families, PND represents oneof the most serious complications of pregnancy (Dagher et al., 2021). Moreover, given the resulting socioeconomic burden, PND is considered a priority target of health prevention strategies globally (Howard and * Corresponding author at: Centre for Chronobiology, Psychiatric Hospital of the University of Basel, Wilhelm Klein-Strasse 27, C

FAQs

What data sources were used to build the machine learning models for PND risk prediction?

The models used sociodemographic variables, blood-based biomarkers, sleep, medical and psychological data, plus polysomnographic parameters recorded during the perinatal period in the Life-ON cohort.

Which factors were identified as the most important predictors of perinatal depression?

The model highlighted first-trimester mood status, previous depressive episodes, and marital status as the most important predictors.

How can early pregnancy sleep changes contribute to identifying women at higher risk?

Sleep quality and insomnia symptoms, along with other covariates, improved model performance, suggesting that assessing sleep changes early in pregnancy can help identify higher-risk women and support appropriate therapeutic strategies.

Aug 5, 2026

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