Technology-Based Approaches for Supporting Perinatal Mental Health

Novick, Andrew M.; Kwitowski, Melissa; Dempsey, Jack; Cooke, Danielle; Dempsey, Allison G.

doi:10.1007/s11920-022-01349-w

Cited by 16 publications

(5 citation statements)

References 70 publications

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“…In addition, efforts should be made to reduce the stigma associated with mental health, creating a safe space for women to discuss their experiences without fear of judgment. By addressing these challenges, healthcare providers can improve the accuracy of diagnoses and ensure that women receive appropriate support and treatment for postpartum mood disorders [30].…”

Section: Challenges and Limitations In Diagnosing Postpartum Mood Dis...mentioning

confidence: 99%

Postpartum Mood Disorders: Insights into Diagnosis, Prevention, and Treatment

Garapati,

Jajoo,

Aradhya

et al. 2023

Cureus

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Postpartum mood disorders pose significant challenges to women's mental health and well-being during the postpartum period. This review article provides insights into these disorders' diagnosis, prevention, and treatment. The article begins by discussing the background information on postpartum mood disorders, their significance, and the purpose of understanding them. It then delves into the classification and types of postpartum mood disorders, emphasizing the need for accurate diagnosis and differentiation. Prevalence and incidence rates are explored to highlight the scope and impact of these disorders. The review examines various risk factors associated with postpartum mood disorders, including biological, psychological, and socioeconomic factors. Understanding these risk factors helps identify high-risk populations and guide targeted interventions. Screening and diagnosis of postpartum mood disorders are crucial for early detection and intervention. The article provides an overview of screening tools, highlights the challenges in diagnosis, and emphasizes the importance of early identification for better outcomes. Prevention strategies are explored, including antenatal education, psychosocial support programs, and the role of healthcare professionals in promoting preventive measures. Effective prevention interventions and their outcomes are discussed to guide healthcare providers and policymakers in implementing evidence-based strategies. Treatment approaches for postpartum mood disorders include pharmacological interventions, psychotherapy options, alternative and complementary therapies, and multidisciplinary approaches. The article discusses the effectiveness and considerations of each approach, highlighting the importance of individualized care. Challenges and barriers in diagnosing, preventing, and treating postpartum mood disorders are addressed, including stigma, limited access to healthcare services, and gaps in healthcare provider knowledge and training. Recommendations are provided for healthcare professionals and policymakers to overcome these challenges and improve outcomes. The review concludes by highlighting the need for future research, innovations in prevention and treatment approaches, and collaborative efforts in the field of postpartum mood disorders. Promising areas for research are identified, including long-term outcomes, understanding risk factors, and cultural considerations. The article emphasizes the importance of interdisciplinary collaboration and stakeholder engagement in advancing the field.

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Section: Challenges and Limitations In Diagnosing Postpartum Mood Dis...mentioning

confidence: 99%

Postpartum Mood Disorders: Insights into Diagnosis, Prevention, and Treatment

Garapati,

Jajoo,

Aradhya

et al. 2023

Cureus

View full text Add to dashboard Cite

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“…Specifically, researchers have used electronic health records (EHRs) ( 25 ), administrative claims data ( 26 ), actively and passively collected data from smartphones ( 27 ), social media data ( 27 ), and patient-reported outcomes ( 27 , 28 ), to predict PD, and, in one study, postpartum psychiatric admission. AI-powered chatbots have also become increasingly researched in mental health generally for their potential to deliver psychotherapy ( 29 , 30 ), and may soon be tailored to the unique needs of perinatal individuals ( 31 , 32 ).…”

Section: Introductionmentioning

confidence: 99%

“…Artificial intelligence for perinatal psychiatry is now in the early, proof-of-concept stages, but researchers, health systems, and companies are planning for implementation ( 32 ). The American College of Obstetrics and Gynecology (ACOG) has also issued guidelines highlighting the potential for AI and other technologies to enhance pregnancy-related care ( 33 ).…”

Section: Introductionmentioning

confidence: 99%

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

Turchioe,

Hermann,

Benda

2024

Front. Psychiatry

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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.

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“…Over the last decade, with the advancements in digital health and computational capabilities, numerous studies have focused on technology-based approaches to advance support for perinatal mental health [12,13]. This includes the development of Arti cial Intelligence (AI)-based models, particularly machine learning and deep learning-based models, for predicting perinatal mental-health conditions [14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Decoding Perinatal Mental Health: Investigating Protective and Risk Factors and Predictive Insights for Aboriginal Perinatal Mental Health through Explainable Machine Learning

Wang,

Bennamoun,

Kwok

et al. 2024

Preprint

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Background Perinatal mental health significantly affects mothers, infants, and families. Despite their resilience and strengths, Aboriginal mothers experience disproportionate physical and mental health disparities. These result from historical and ongoing impacts of colonization and the resultant complex trauma. Conventional approaches to perinatal care present many barriers for Aboriginal mothers who frequently feel disengaged, apprehensive and unsafe. Current score-based risk-screening practices that algorithmically drive referrals, further ingrain fears including culturally biased judgments and child removal. The Baby Coming You Ready (BCYR) model of care centred around a digitised, holistic, strengths-based assessment, was co-designed to address these barriers. The recent successful pilot demonstrated BCYR effectively replaced all current risk-based screens. However, many professionals disproportionately rely on psychological risk scores, overlooking the contextual circumstances of Aboriginal mothers, their cultural strengths and mitigating protective factors. Methods To address this singular reliance screening psychometrics whilst supporting strengthened culturally considered clinical assessment, we propose a culturally sensitive eXplainable AI (XAI) solution. It combines XAI with Aboriginal lived experience, knowledge and wisdom to generate a clinical prediction model to support professionals and Aboriginal mothers being screened. The XAI solution can identify, prioritise, and weigh both maternal protective strengths and risk factors, quantify their relative impacts on perinatal mental-health and well-being at both group and individual levels. Results Different machine learning algorithms, including Random Forest, K-nearest neighbour, and support vector machine, alongside glassbox Explainable Boosting Machine (EBM) models, were trained on the real life de-identified data generated during the BCYR pilot. Additionally, XAI techniques like SHAP and LIME are utilised for interpretability on black box models. Results show the EBM model demonstrates superior performance in prediction, with an accuracy of 0.849, F1 score of 0.771 and AUC of 0.821. Global explanations across the entire dataset and local explanations for individual cases, achieved through different methods, were compared and showed similar and stable results. Conclusions This study demonstrated the potential for this XAI solution to enhance professionals' capability in culturally responsive clinical reasoning in perinatal mental-health screening to improve experience and strengthen outcomes for Aboriginal women.

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Technology-Based Approaches for Supporting Perinatal Mental Health

Cited by 16 publications

References 70 publications

Postpartum Mood Disorders: Insights into Diagnosis, Prevention, and Treatment

Postpartum Mood Disorders: Insights into Diagnosis, Prevention, and Treatment

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

Decoding Perinatal Mental Health: Investigating Protective and Risk Factors and Predictive Insights for Aboriginal Perinatal Mental Health through Explainable Machine Learning

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