Machine learning in the prediction of postpartum depression: A review

Predicting peripartum depression using elastic net regression and machine learning: the role of remnant cholesterol

BACKGROUND

Traditional statistical methods have dominated research on peripartum depression (PPD), but innovative approaches may provide deeper insights. This study aims to predict the impact factors of PPD using elastic net regression (ENR) combined with machine learning (ML) model.

METHODS

This longitudinal study was conducted from June 2020 to May 2023, involving healthy pregnant women in the first trimester, followed up until the completion of the assessment in the second trimester. PPD symptoms were assessed using the Edinburgh Postnatal Depression Scale (EPDS). Features with p <.05 from logistic regression were selected and refined using ENR. These features were then used to build six ML models to identify the best-performing one. SHapley Additive exPlanations (SHAP) analysis was employed to enhance model interpretability by visualizing its decision-making process.

RESULTS

A total of 608 participants were followed, resulting in 384 valid questionnaires. After excluding incomplete or incorrect baseline data, 325 participants were ultimately included in the study. Among these, 130 were classified as having mild depression, and 32 were classified with major depression. Nineteen features were initially identified as being associated with PPD, with 14 retained after ENR refinement. The random forest (RF) model outperformed the other ML models. SHAP analysis identified the top five predictors of PPD: magnesium (Mg), remnant cholesterol (RC), calcium (Ca), mean corpuscular hemoglobin concentration (MCHc), and potassium (K). Mg, Ca, MCHc, and K were negatively correlated with PPD, while RC showed a positive correlation.

CONCLUSIONS

The RF model effectively identified associations between exposure factors and PPD. Mg, Ca, MCHc, and K were found to be protective factors, while RC emerged as a potential risk factor, highlighting its potential as a novel biomarker for PPD.

Investigating Protective and Risk Factors and Predictive Insights for Aboriginal Perinatal Mental Health: Explainable Artificial Intelligence Approach

BACKGROUND

Perinatal depression and anxiety significantly impact maternal and infant health, potentially leading to severe outcomes like preterm birth and suicide. Aboriginal women, despite their resilience, face elevated risks due to the long-term effects of colonization and cultural disruption. The Baby Coming You Ready (BCYR) model of care, centered on a digitized, holistic, strengths-based assessment, was co-designed to address these challenges. The successful BCYR pilot demonstrated its ability to replace traditional risk-based screens. However, some health professionals still overrely on psychological risk scores, often overlooking the contextual circumstances of Aboriginal mothers, their cultural strengths, and mitigating protective factors. This highlights the need for new tools to improve clinical decision-making.

OBJECTIVE

We explored different explainable artificial intelligence (XAI)-powered machine learning techniques for developing culturally informed, strengths-based predictive modeling of perinatal psychological distress among Aboriginal mothers. The model identifies and evaluates influential protective and risk factors while offering transparent explanations for AI-driven decisions.

METHODS

We used deidentified data from 293 Aboriginal mothers who participated in the BCYR program between September 2021 and June 2023 at 6 health care services in Perth and regional Western Australia. The original dataset includes variables spanning cultural strengths, protective factors, life events, worries, relationships, childhood experiences, family and domestic violence, and substance use. After applying feature selection and expert input, 20 variables were chosen as predictors. The Kessler-5 scale was used as an indicator of perinatal psychological distress. Several machine learning models, including random forest (RF), CatBoost (CB), light gradient-boosting machine (LightGBM), extreme gradient boosting (XGBoost), k-nearest neighbor (KNN), support vector machine (SVM), and explainable boosting machine (EBM), were developed and compared for predictive performance. To make the black-box model interpretable, post hoc explanation techniques including Shapley additive explanations and local interpretable model-agnostic explanations were applied.

RESULTS

The EBM outperformed other models (accuracy=0.849, 95% CI 0.8170-0.8814; F1-score=0.771, 95% CI 0.7169-0.8245; area under the curve=0.821, 95% CI 0.7829-0.8593) followed by RF (accuracy=0.829, 95% CI 0.7960-0.8617; F1-score=0.736, 95% CI 0.6859-0.7851; area under the curve=0.795, 95% CI 0.7581-0.8318). Explanations from EBM, Shapley additive explanations, and local interpretable model-agnostic explanations identified consistent patterns of key influential factors, including questions related to "Feeling Lonely," "Blaming Herself," "Makes Family Proud," "Life Not Worth Living," and "Managing Day-to-Day." At the individual level, where responses are highly personal, these XAI techniques provided case-specific insights through visual representations, distinguishing between protective and risk factors and illustrating their impact on predictions.

CONCLUSIONS

This study shows the potential of XAI-driven models to predict psychological distress in Aboriginal mothers and provide clear, human-interpretable explanations of how important factors interact and influence outcomes. These models may help health professionals make more informed, non-biased decisions in Aboriginal perinatal mental health screenings.

Predictive Analysis of Postpartum Depression Using Machine Learning

Background: Maternal postpartum depression (PPD) is a major psychological problem affecting mothers, newborns, and their families after childbirth. This study investigated the factors influencing maternal PPD and developed a predictive model using machine learning. Methods/Design: In this study, we applied machine learning techniques to identify significant predictors of PPD and to develop a model for classifying individuals at risk. Data from 2570 subjects were analyzed using the Korean Early Childhood Education and Care Panel (K-ECEC-P) dataset as of January 2025, utilizing Python version 3.12.8. Results: We compared the performance of a decision tree classifier, random forest classifier, AdaBoost classifier, and logistic regression model using metrics such as precision, accuracy, recall, F1-score, and area under the curve. The logistic regression model was selected as the best model. Among the 13 features analyzed, conflict with a partner, stress, and the value of children emerged as significant predictors of PPD. Discussion: Conflict with a partner and stress levels emerged as the strongest predictors. Higher levels of conflict and stress were associated with an increased likelihood of PPD, whereas a higher value of children reduced this risk. Maternal psychological status and environmental features should be managed carefully during the postpartum period.

Prediction of postpartum depression in women: development and validation of multiple machine learning models

BACKGROUND

Postpartum depression (PPD) is a significant public health issue. This study aimed to develop and validate machine learning (ML) models using biopsychosocial predictors to predict the risk of PPD for perinatal women and to provide several risk assessment tools for the early detection of PPD.

METHODS

Candidate predictors, including history of mental illness and demographic, psychosocial, and physiological factors, were obtained from 1138 perinatal women between August 2021 and August 2022. The primary outcome of PPD was measured with the Edinburgh Postnatal Depression Scale at 6 weeks postpartum. Seven feature selection methods and six ML algorithms were employed to develop models, and their prediction performances were compared.

RESULTS

A total of 11 potential predictive factors associated with PPD were identified and subsequently used to construct prenatal and postpartum predictive models for PPD. The cross-validation results showed that the models built on logistic regression (LR) [area under the curve (AUC): 0.801, 0.858] and artificial neural network (ANN) (AUC: 0.787, 0.844) algorithms exhibited the best prediction performance. In contrast to the prenatal models, the addition of postpartum predictors (primary caregiver and mother-in-law's care) remarkably improved the predictive performance of the postpartum models. The risk-stratification score, the nomogram, and the Shapley additive explanation were used to visualize and interpret the risk prediction model for predicting PPD in the early stage.

CONCLUSIONS

The LR and ANN models achieved the best predictive performances. Applying these models and risk assessment tools to early predict and screen PPD has several implications for public health.

Decision tree learning for predicting chronic postpartum depression in the Japan Environment and Children's Study

BACKGROUND

Many studies have used machine learning techniques to construct predictive models of postpartum depression, but few such models are simple enough to use in community maternal health settings with pen and paper. Here, we use a decision tree to construct a prediction model for chronic postpartum depression.

METHODS

Participants were 84,091 mothers. Chronic postpartum depression was identified as an Edinburgh Postnatal Depression Scale score of ≥9 at both 1 and 6 months postpartum. The training dataset included 84 diverse variables measured during pregnancy, including health status and biomarkers. In learning, the branching depth was constrained to 3, the number of branches per branch to 4, and the minimum number of n in a branch was 100. The training to validation data ratio was set to 7:3.

RESULTS

A decision tree with 35 branches and an area under the receiver operating characteristic of 0.84 was created. Ten of 84 variables were extracted, and the most effective in classification was "feeling worthless." At training (n = 58,635), the most and least prevalent branches were 73.2 % and 0.84 % (mean = 6.29 %), respectively; at validation (n = 25,456), they were 60.4 % and 0.72 % (mean = 6.52 %), respectively.

LIMITATIONS

Chronic postpartum depression was identified using self-administered questionnaires.

CONCLUSIONS

This study created a simple and relatively high-performing prediction model. Because the model can be easily understood and used without expertise in machine learning, it is expected to be useful in maternal health settings, including grassroots community health.

Predicting first time depression onset in pregnancy: applying machine learning methods to patient-reported data

PURPOSE

To develop a machine learning algorithm, using patient-reported data from early pregnancy, to predict later onset of first time moderate-to-severe depression.

METHODS

A sample of 944 U.S. patient participants from a larger longitudinal observational cohortused a prenatal support mobile app from September 2019 to April 2022. Participants self-reported clinical and social risk factors during first trimester initiation of app use and completed voluntary depression screenings in each trimester. Several machine learning algorithms were applied to self-reported data, including a novel algorithm for causal discovery. Training and test datasets were built from a randomized 80/20 data split. Models were evaluated on their predictive accuracy and their simplicity (i.e., fewest variables required for prediction).

RESULTS

Among participants, 78% identified as white with an average age of 30 [IQR 26-34]; 61% had income ≥ $50,000; 70% had a college degree or higher; and 49% were nulliparous. All models accurately predicted first time moderate-severe depression using first trimester baseline data (AUC 0.74-0.89, sensitivity 0.35-0.81, specificity 0.78-0.95). Several predictors were common across models, including anxiety history, partnered status, psychosocial factors, and pregnancy-specific stressors. The optimal model used only 14 (26%) of the possible variables and had excellent accuracy (AUC = 0.89, sensitivity = 0.81, specificity = 0.83). When food insecurity reports were included among a subset of participants, demographics, including race and income, dropped out and the model became more accurate (AUC = 0.93) and simpler (9 variables).

CONCLUSION

A relatively small amount of self-report data produced a highly predictive model of first time depression among pregnant individuals.

The pathophysiology of estrogen in perinatal depression: conceptual update

PURPOSE

Estrogen levels fall sharply after parturition and have long been considered an etiologic contributor to postpartum depression (PPD); however, no differences have been reported in plasma hormone concentrations in people who develop PPD. We examine the question: What is the current view of estrogen and the neurophysiologic processes it impacts in the development and treatment of PPD?

METHODS

A literature review of the role of estrogen on candidate hormonal and epigenetic systems in the peripartum period was performed, including landmark historical studies and recent publications on estrogen-related research. The authors reviewed these papers and participated in reaching consensus on a conceptual framework of estrogen activity within the complexity of pregnancy physiology to examine its potential role for driving novel interventions.

RESULTS

Estrogen fluctuations must be conceptualized in the context of multiple dramatic and interacting changes inherent in pregnancy and after birth, including progesterone, corticosteroids, inflammation, circadian biology and psychosocial challenges. Individuals who develop PPD have increased sensitivity to epigenetic alteration at estrogen-responsive genes, and these changes are highly predictive of PPD. An effective estrogen-based treatment for PPD has yet to be found, but interventions focused on associated inflammation and circadian rhythms are promising.

CONCLUSIONS

Our understanding of the biological basis of PPD, one of the most common morbidities of the perinatal period, is expanding beyond changes in gynecologic hormone concentrations to include their impact on other systems. This growing understanding of the many processes influencing PPD will allow for the development of novel prevention and treatment strategies.

Birthing consciousness and the flow experience during physiological childbirth

PROBLEM

It has been demonstrated that birth without medical intervention conveys significant physical and psychological benefits to the mother and her newborn baby. However, there is a need to include women's subjective experience of physiological birth to understand and promote it.

BACKGROUND

The theoretical concept of "birthing consciousness" hypothesizes that women during natural childbirth sometimes experience a specific altered state of consciousness, which is a positive peak experience that resembles "flow" in many aspects.

AIM

To investigate the underexplored connection between the physiological mode of childbirth and altered states of consciousness during childbirth.

METHODS

Israeli women with childbirth experience were recruited through social media (Facebook groups with a focus on childbirth and motherhood). Participants (n = 766) completed an online survey: the Flow State Scale (FSS) and a demographic questionnaire.

FINDINGS

Differences were found between modes of birth as to flow state, as women who experienced physiological childbirth (i.e., with no epidural anesthesia or instrumental interventions) had a higher flow state during birth.

DISCUSSION

This link empirically confirms the phenomenon of birthing consciousness. All nine dimensions of the mental state of flow apply to childbirth: challenge-skill balance, action-awareness merging, clear goals, unambiguous feedback, concentration on the task, sense of control, loss of self-consciousness, transformation of time, and autotelic experience.

CONCLUSION

Understanding a women's subjective experience during physiological birth can enhance clinical understanding of physiological birth thus promoting positive physiological birth experiences - which has crucial health benefits. We propose that more studies need to be done to promote experiencing flow during physiological birth.

Digital phenotyping of depression during pregnancy using self-report data

BACKGROUND

Depression is a common pregnancy complication yet is often under-detected and, subsequently, undertreated. Data collected through mobile health tools may be used to support the identification of depression symptoms in pregnancy.

METHODS

An observational cohort study of 2062 pregnancies collected self-reports of patient history, mood, pregnancy-specific symptoms, and written language using a prenatal support app. These app inputs were used to model depression risk in subsequent 30- and 60-day periods throughout pregnancy. A selective inference lasso modeling approach examined the individual and additive value of each type of patient-reported app input.

RESULTS

Depression models ranged in predictive power (AUC value of 0.64-0.83), depending on the type of inputs. The most predictive model included personal history, daily mood, and acute pregnancy-related symptoms (e.g., severe vomiting, cramping). Across models, daily mood was the strongest indicator of depression symptoms in the following month. Models that retained natural language inputs typically improved predictive accuracy and offered insight into the lived context associated with experiencing depression.

LIMITATIONS

Our findings are not generalizable beyond a digitally literate patient population that is self-motivated to report data during pregnancy.

CONCLUSIONS

Simple patient reported data, including sparse language, shared directly via digital tools may support earlier depression symptom identification and a more nuanced understanding of depression context.

Prediction of adolescent depression from prenatal and childhood data from ALSPAC using machine learning

Depression is a major cause of disability and mortality for young people worldwide and is typically first diagnosed during adolescence. In this work, we present a machine learning framework to predict adolescent depression occurring between ages 12 and 18 years using environmental, biological, and lifestyle features of the child, mother, and partner from the child's prenatal period to age 10 years using data from 8467 participants enrolled in the Avon Longitudinal Study of Parents and Children (ALSPAC). We trained and compared several cross-sectional and longitudinal machine learning techniques and found the resulting models predicted adolescent depression with recall (0.59 ± 0.20), specificity (0.61 ± 0.17), and accuracy (0.64 ± 0.13), using on average 39 out of the 885 total features (4.4%) included in the models. The leading informative features in our predictive models of adolescent depression were female sex, parental depression and anxiety, and exposure to stressful events or environments. This work demonstrates how using a broad array of evidence-driven predictors from early in life can inform the development of preventative decision support tools to assist in the early detection of risk for mental illness.

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

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.

Machine learning techniques for prediction in pregnancy complicated by autoimmune rheumatic diseases: Applications and challenges

Autoimmune rheumatic diseases are chronic conditions affecting multiple systems and often occurring in young women of childbearing age. The diseases and the physiological characteristics of pregnancy significantly impact maternal-fetal health and pregnancy outcomes. Currently, the integration of big data with healthcare has led to the increasing popularity of using machine learning (ML) to mine clinical data for studying pregnancy complications. In this review, we introduce the basics of ML and the recent advances and trends of ML in different prediction applications for common pregnancy complications by autoimmune rheumatic diseases. Finally, the challenges and future for enhancing the accuracy, reliability, and clinical applicability of ML in prediction have been discussed. This review will provide insights into the utilization of ML in identifying and assisting clinical decision-making for pregnancy complications, while also establishing a foundation for exploring comprehensive management strategies for pregnancy and enhancing maternal and child health.

Comparative analysis of machine learning versus traditional method for early detection of parental depression symptoms in the NICU

Introduction

Neonatal intensive care unit (NICU) admission is a stressful experience for parents. NICU parents are twice at risk of depression symptoms compared to the general birthing population. Parental mental health problems have harmful long-term effects on both parents and infants. Timely screening and treatment can reduce these negative consequences.

Objective

Our objective is to compare the performance of the traditional logistic regression with other machine learning (ML) models in identifying parents who are more likely to have depression symptoms to prioritize screening of at-risk parents. We used data obtained from parents of infants discharged from the NICU at Children's National Hospital (n = 300) from 2016 to 2017. This dataset includes a comprehensive list of demographic characteristics, depression and stress symptoms, social support, and parent/infant factors.

Study design

Our study design optimized eight ML algorithms - Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, XGBoost, Naïve Bayes, K-Nearest Neighbor, and Artificial Neural Network - to identify the main risk factors associated with parental depression. We compared models based on the area under the receiver operating characteristic curve (AUC), positive predicted value (PPV), sensitivity, and F-score.

Results

The results showed that all eight models achieved an AUC above 0.8, suggesting that the logistic regression-based model's performance is comparable to other common ML models.

Conclusion

Logistic regression is effective in identifying parents at risk of depression for targeted screening with a performance comparable to common ML-based models.

A novel machine learning-based prediction method for patients at risk of developing depressive symptoms using a small data

The prediction of depression is a crucial area of research which makes it one of the top priorities in mental health research as it enables early intervention and can lead to higher success rates in treatment. Self-reported feelings by patients represent a valuable biomarker for predicting depression as they can be expressed in a lower-dimensional network form, offering an advantage in visualizing the interactive characteristics of depression-related feelings. Furthermore, the network form of data expresses high-dimensional data in a compact form, making the data easy to use as input for the machine learning processes. In this study, we applied the graph convolutional network (GCN) algorithm, an effective machine learning tool for handling network data, to predict depression-prone patients using the network form of self-reported log data as the input. We took a data augmentation step to expand the initially small dataset and fed the resulting data into the GCN algorithm, which achieved a high level of accuracy from 86-97% and an F1 (harmonic mean of precision and recall) score of 0.83-0.94 through three experimental cases. In these cases, the ratio of depressive cases varied, and high accuracy and F1 scores were observed in all three cases. This study not only demonstrates the potential for predicting depression-prone patients using self-reported logs as a biomarker in advance, but also shows promise in handling small data sets in the prediction, which is critical given the challenge of obtaining large datasets for biomarker research. The combination of self-reported logs and the GCN algorithm is a promising approach for predicting depression and warrants further investigation.

Prevalence and risk factors analysis of postpartum depression at early stage using hybrid deep learning model

Postpartum Depression Disorder (PPDD) is a prevalent mental health condition and results in severe depression and suicide attempts in the social community. Prompt actions are crucial in tackling PPDD, which requires a quick recognition and accurate analysis of the probability factors associated with this condition. This concern requires attention. The primary aim of our research is to investigate the feasibility of anticipating an individual's mental state by categorizing individuals with depression from those without depression using a dataset consisting of text along with audio recordings from patients diagnosed with PPDD. This research proposes a hybrid PPDD framework that combines Improved Bi-directional Long Short-Term Memory (IBi-LSTM) with Transfer Learning (TL) based on two Convolutional Neural Network (CNN) architectures, respectively CNN-text and CNN audio. In the proposed model, the CNN section efficiently utilizes TL to obtain crucial knowledge from text and audio characteristics, whereas the improved Bi-LSTM module combines written material and sound data to obtain intricate chronological interpersonal relationships. The proposed model incorporates an attention technique to augment the effectiveness of the Bi-LSTM scheme. An experimental analysis is conducted on the PPDD online textual and speech audio dataset collected from UCI. It includes textual features such as age, women's health tracks, medical histories, demographic information, daily life metrics, psychological evaluations, and 'speech records' of PPDD patients. Data pre-processing is applied to maintain the data integrity and achieve reliable model performance. The proposed model demonstrates a great performance in better precision, recall, accuracy, and F1-score over existing deep learning models, including VGG-16, Base-CNN, and CNN-LSTM. These metrics indicate the model's ability to differentiate among women at risk of PPDD vs. non-PPDD. In addition, the feature importance analysis demonstrates that specific risk factors substantially impact the prediction of PPDD. The findings of this research establish a basis for improved precision and promptness in assessing the risk of PPDD, which may ultimately result in earlier implementation of interventions and the establishment of support networks for women who are susceptible to PPDD.

Explainable Machine Learning Classification to Identify Vulnerable Groups Among Parenting Mothers: Web-Based Cross-Sectional Questionnaire Study

BACKGROUND

One life event that requires extensive resilience and adaptation is parenting. However, resilience and perceived support in child-rearing vary, making the real-world situation unclear, even with postpartum checkups.

OBJECTIVE

This study aimed to explore the psychosocial status of mothers during the child-rearing period from newborn to toddler, with a classifier based on data on the resilience and adaptation characteristics of mothers with newborns.

METHODS

A web-based cross-sectional survey was conducted. Mothers with newborns aged approximately 1 month (newborn cohort) were analyzed to construct an explainable machine learning classifier to stratify parenting-related resilience and adaptation characteristics and identify vulnerable populations. Explainable k-means clustering was used because of its high explanatory power and applicability. The classifier was applied to mothers with infants aged 2 months to 1 year (infant cohort) and mothers with toddlers aged >1 year to 2 years (toddler cohort). Psychosocial status, including depressed mood assessed by the Edinburgh Postnatal Depression Scale (EPDS), bonding assessed by the Postpartum Bonding Questionnaire (PBQ), and sleep quality assessed by the Pittsburgh Sleep Quality Index (PSQI) between the classified groups, was compared.

RESULTS

A total of 1559 participants completed the survey. They were split into 3 cohorts, comprising populations of various characteristics, including parenting difficulties and psychosocial measures. The classifier, which stratified participants into 5 groups, was generated from the self-reported scores of resilience and adaptation in the newborn cohort (n=310). The classifier identified that the group with the greatest difficulties in resilience and adaptation to a child's temperament and perceived support had higher incidences of problems with depressed mood (relative prevalence [RP] 5.87, 95% CI 2.77-12.45), bonding (RP 5.38, 95% CI 2.53-11.45), and sleep quality (RP 1.70, 95% CI 1.20-2.40) compared to the group with no difficulties in perceived support. In the infant cohort (n=619) and toddler cohort (n=461), the stratified group with the greatest difficulties had higher incidences of problems with depressed mood (RP 9.05, 95% CI 4.36-18.80 and RP 4.63, 95% CI 2.38-9.02, respectively), bonding (RP 1.63, 95% CI 1.29-2.06 and RP 3.19, 95% CI 2.03-5.01, respectively), and sleep quality (RP 8.09, 95% CI 4.62-16.37 and RP 1.72, 95% CI 1.23-2.42, respectively) compared to the group with no difficulties.

CONCLUSIONS

The classifier, based on a combination of resilience and adaptation to the child's temperament and perceived support, was able identify psychosocial vulnerable groups in the newborn cohort, the start-up stage of childcare. Psychosocially vulnerable groups were also identified in qualitatively different infant and toddler cohorts, depending on their classifier. The vulnerable group identified in the infant cohort showed particularly high RP for depressed mood and poor sleep quality.

Racial Disparities in Diagnosis of Postpartum Mood and Anxiety Disorders Among Symptomatic Medicaid Enrollees, 2012-2015

OBJECTIVE

This study quantified the prevalence of postpartum mood and anxiety disorder (PMAD) diagnoses among symptomatic Michigan Medicaid enrollees and explored factors associated with receiving a diagnosis.

METHODS

Data sources comprised Michigan Medicaid administrative claims and Phase 7 Michigan Pregnancy Risk Assessment Monitoring System (MI-PRAMS) survey responses, linked at the individual level. Participants were continuously enrolled in Michigan Medicaid, delivered a live birth (2012-2015), responded to the survey, and screened positive for PMAD symptoms on the adapted two-item Patient Health Questionnaire. Unadjusted and adjusted weighted logistic regression analyses were used to predict the likelihood of having a PMAD diagnosis (for the overall sample and stratified by race).

RESULTS

The weighted analytic cohort represented 24,353 deliveries across the 4-year study. Only 19.8% of respondents with symptoms of PMAD had a PMAD diagnosis between delivery and 3 months afterward. Black respondents were less likely to have PMAD diagnoses (adjusted odds ratio [AOR]=0.23, 95% CI=0.11-0.49) compared with White respondents. Among White respondents, no covariates were significantly associated with having a diagnosis. However, among Black respondents, more comorbid conditions and more life stressors were statistically significantly associated with having a diagnosis (AOR=3.18, 95% CI=1.27-7.96 and AOR=3.12, 95% CI=1.10-8.88, respectively).

CONCLUSIONS

Rate of PMAD diagnosis receipt differed by race and was low overall. Black respondents were less likely than White respondents to receive a diagnosis. Patient characteristics influencing diagnosis receipt also differed by race, indicating that strategies to improve detection of these disorders require a tailored approach.

Optimal risk and diagnosis assessment strategies in perinatal depression: A machine learning approach from the life-ON study cohort

This study aimed to assess the concordance of various psychometric scales in detecting Perinatal Depression (PND) risk and diagnosis. A cohort of 432 women was assessed at 10-15th and 23-25th gestational weeks, 33-40 days and 180-195 days after delivery using the Edinburgh Postnatal Depression Scale (EPDS), Visual Analogue Scale (VAS), Hamilton Depression Rating Scale (HDRS), Montgomery-Åsberg Depression Rating Scale (MADRS), and Mini International Neuropsychiatric Interview (MINI). Spearman's rank correlation coefficient was used to assess agreement across instruments, and multivariable classification models were developed to predict the values of a binary scale using the other scales. Moderate agreement was shown between the EPDS and VAS and between the HDRS and MADRS throughout the perinatal period. However, agreement between the EPDS and HDRS decreased postpartum. A well-performing model for the estimation of current depression risk (EPDS > 9) was obtained with the VAS and MADRS, and a less robust one for the estimation of current major depressive episode (MDE) diagnosis (MINI) with the VAS and HDRS. When the EPDS is not feasible, the VAS may be used for rapid and comprehensive postpartum screening with reliability. However, a thorough structured interview or clinical examination remains necessary to diagnose a MDE.

Recentering responsible and explainable artificial intelligence research on patients: implications in perinatal psychiatry

In the setting of underdiagnosed and undertreated perinatal depression (PD), Artificial intelligence (AI) solutions are poised to help predict and treat PD. In the near future, perinatal patients may interact with AI during clinical decision-making, in their patient portals, or through AI-powered chatbots delivering psychotherapy. The increase in potential AI applications has led to discussions regarding responsible AI and explainable AI (XAI). Current discussions of RAI, however, are limited in their consideration of the patient as an active participant with AI. Therefore, we propose a patient-centered, rather than a patient-adjacent, approach to RAI and XAI, that identifies autonomy, beneficence, justice, trust, privacy, and transparency as core concepts to uphold for health professionals and patients. We present empirical evidence that these principles are strongly valued by patients. We further suggest possible design solutions that uphold these principles and acknowledge the pressing need for further research about practical applications to uphold these principles.

Predicting new-onset post-stroke depression from real-world data using machine learning algorithm

Introduction

Post-stroke depression (PSD) is a serious mental disorder after ischemic stroke. Early detection is important for clinical practice. This research aims to develop machine learning models to predict new-onset PSD using real-world data.

Methods

We collected data for ischemic stroke patients from multiple medical institutions in Taiwan between 2001 and 2019. We developed models from 61,460 patients and used 15,366 independent patients to test the models' performance by evaluating their specificities and sensitivities. The predicted targets were whether PSD occurred at 30, 90, 180, and 365 days post-stroke. We ranked the important clinical features in these models.

Results

In the study's database sample, 1.3% of patients were diagnosed with PSD. The average specificity and sensitivity of these four models were 0.83-0.91 and 0.30-0.48, respectively. Ten features were listed as important features related to PSD at different time points, namely old age, high height, low weight post-stroke, higher diastolic blood pressure after stroke, no pre-stroke hypertension but post-stroke hypertension (new-onset hypertension), post-stroke sleep-wake disorders, post-stroke anxiety disorders, post-stroke hemiplegia, and lower blood urea nitrogen during stroke.

Discussion

Machine learning models can provide as potential predictive tools for PSD and important factors are identified to alert clinicians for early detection of depression in high-risk stroke patients.

An optimization for postpartum depression risk assessment and preventive intervention strategy based machine learning approaches

BACKGROUND

Postpartum depression (PPD) is one of the most common psychiatric disorders for women after delivery. The establishment of an effective PPD prediction model helps to distinguish high-risk groups, and verifying whether such high-risk groups can benefit from drug intervention is very important for clinical guidance.

METHODS

We collected data of parturients that underwent a cesarean delivery. The Control group was divided into a training cohort and a testing cohort. Six different ML models were constructed and we compared their prediction performance in the testing cohort. For model interpretation, we introduced SHapley Additive exPlanations (SHAP). Then, training cohort, ketamine group and dexmedetomidine (DEX) group were classified as high or low risk for PPD by the model. A 1:1 propensity score matching (PSM) was performed to compare the incidence of PPD between two groups in different risk cohorts.

RESULTS

Extreme gradient enhancement (XGB) had the best recognition effect, with an area under the receiver operating characteristic curve (AUROC) of 0.789 (95 % CI 0.742-0.836) in the training cohort and 0.744 (95 % CI 0.655-0.823) in the testing cohort, respectively. A threshold of 21.5 % PPD risk probability was determined. After PSM, the results showed that the incidence of PPD in the two intervention groups was significantly different from the control group in the high-risk cohort (P < 0.001) but not in the low-risk cohort (P > 0.001).

CONCLUSION

Our study demonstrated that the XGB algorithm provided a more accurate in prediction of PPD risk, and it was beneficial to receive early intervention for the high-risk groups distinguished by the model.

Trajectories of Maternal and Paternal Psychological Distress After Fetal Diagnosis of Moderate-Severe Congenital Heart Disease

OBJECTIVE

The aim of this study was to compare trajectories of maternal and paternal psychological distress after prenatal diagnosis of fetal moderate-severe congenital heart disease (CHD), from pregnancy through early-mid infancy.

METHODS

Pregnant women who received a prenatal diagnosis of fetal moderate-severe CHD, and their partners, were enrolled in a prospective, longitudinal study. Symptoms of psychological distress were measured twice during pregnancy and twice after birth, using the Depression Anxiety Stress Scales (DASS-42). Patterns and predictors of psychological distress were examined using generalized hierarchical linear modeling.

RESULTS

Psychological distress was present in 42% (18/43) of mothers and 22% (8/36) of fathers at least once during the study. The rates of distress did not differ between mothers and fathers. There was also no change in probability of distress over time or difference in distress trajectories between mothers and fathers. However, individual trajectories demonstrated considerable variability in symptoms for both mothers and fathers. Predictors of psychological distress included low social support for mothers and a history of mental health conditions for fathers.

CONCLUSIONS

Parents who receive a prenatal diagnosis of fetal CHD commonly report symptoms of psychological distress from the time of diagnosis through early-mid infancy and display highly variable trajectories. These data suggest that early and repeated psychological screening is important once a fetal CHD diagnosis is made and that providing mental health and social support to parents may be an important component of their ongoing care.

Application of machine learning in predicting the risk of postpartum depression: A systematic review

BACKGROUND

Postpartum depression (PPD) presents a serious health problem among women and their families. Machine learning (ML) is a rapidly advancing field with increasing utility in predicting PPD risk. We aimed to synthesize and evaluate the quality of studies on application of ML techniques in predicting PPD risk.

METHODS

We conducted a systematic search of eight databases, identifying English and Chinese studies on ML techniques for predicting PPD risk and ML techniques with performance metrics. Quality of the studies involved was evaluated using the Prediction Model Risk of Bias Assessment Tool.

RESULTS

Seventeen studies involving 62 prediction models were included. Supervised learning was the main ML technique employed and the common ML models were support vector machine, random forest and logistic regression. Five studies (30 %) reported both internal and external validation. Two studies involved model translation, but none were tested clinically. All studies showed a high risk of bias, and more than half showed high application risk.

LIMITATIONS

Including Chinese articles slightly reduced the reproducibility of the review. Model performance was not quantitatively analyzed owing to inconsistent metrics and the absence of methods for correlation meta-analysis.

CONCLUSIONS

Researchers have paid more attention to model development than to validation, and few have focused on improvement and innovation. Models for predicting PPD risk continue to emerge. However, few have achieved the acceptable quality standards. Therefore, ML techniques for successfully predicting PPD risk are yet to be deployed in clinical environments.

Inflammatory pathophysiological mechanisms implicated in postpartum depression

Postpartum Depression (PPD) is a serious psychiatric disorder of women within the first year after delivery. It grievously damages women's physical and mental health. Inflammatory reaction theory is well-established in depression, and also has been reported associated with PPD. This review summarized the inflammatory pathophysiological mechanisms implicated in PPD, including decreased T cell activation, increased proinflammatory cytokines secretion, active kynurenine pathway, and initiated NLRP3 inflammasome. Clinical and preclinical research are both gathered. Potential therapeutical alternatives targeting the inflammatory mechanisms of PPD were introduced. In addition, this review briefly discussed the differences of inflammatory mechanisms between PPD and depression. The research of inflammation in PPD is limited and seems just embarking, which indicates the direction we can further study. As a variety of risky factors contribute to PPD collectively, therapy for women with PPD should be comprehensive, and clinical heterogeneity should be taken into consideration. As PPD has a predictability, early clinical screening and interventions are also needed. This review aims to help readers better understand the inflammatory pathological mechanisms in PPD, so as to identify biomarkers and potential therapeutic targets in the future.

Postpartum Depression-Identifying Risk and Access to Intervention

PURPOSE OF REVIEW

As maternal mortality climbs in the USA with mental health conditions driving these preventable deaths, the field of reproductive psychiatry must shift towards identification of women and other birthing individuals at risk and facilitating access. This review brings together recent studies regarding risk of perinatal depression and highlights important comorbidities that place individuals at higher vulnerability to poor perinatal outcomes.

RECENT FINDINGS

Recent research suggests that identifying risk for perinatal depression including historical diagnoses of depression, anxiety, trauma, and comorbid substance use and intimate partner violence may move the field to focus on preventive care in peripartum populations. Emerging data shows stark health inequities in racial and ethnic minority populations historically marginalized by the health system and in other vulnerable groups such as LGBTQ+ individuals and those with severe mental illness. Innovative models of care using systems-level approaches can provide opportunities for identification and risk analyses of vulnerable peripartum patients and facilitate access to therapeutic or preventive interventions. Utilizing intergenerational approaches and leveraging multidisciplinary teams that thoughtfully target high-risk women and other birthing individuals could promote significant changes to population-level care in maternal health.