Supporting Personalized prEgnancy Care wIth Artificial inteLligence (SPECIAL): An Acceptability Study of a Personalized Educational Platform.

Postpartum depression (PPD) affects approximately 20% of pregnant individuals, yet half of these cases remain under-treated despite the availability of educational interventions. To address this gap, the Supporting Personalized prEgnancy Care wIth Artificial inteLligence (SPECIAL) project leverages Artificial Intelligence (AI) to deliver personalized health education materials for PPD prevention and management. This study evaluated the acceptability of a web-based prototype in SPECIAL, developed with patient input, using the Unified Theory of Acceptance of Use of Technology (UTAUT) framework.

Differing perspectives on artificial intelligence in mental healthcare among patients: a cross-sectional survey study.

Introduction: Artificial intelligence (AI) is being developed for mental healthcare, but patients' perspectives on its use are unknown. This study examined differences in attitudes towards AI being used in mental healthcare by history of mental illness, current mental health status, demographic characteristics, and social determinants of health.

Methods: We conducted a cross-sectional survey of an online sample of 500 adults asking about general perspectives, comfort with AI, specific concerns, explainability and transparency, responsibility and trust, and the importance of relevant bioethical constructs.

Implementation of a Machine Learning Risk Prediction Model for Postpartum Depression in the Electronic Health Records.

This study describes the deployment process of an AI-driven clinical decision support (CDS) system to support postpartum depression (PPD) prevention, diagnosis and management. Central to this CDS is an L2-regularized logistic regression model trained on electronic health record (EHR) data at an academic medical center, and subsequently refined through a broader dataset from a consortium to ensure its generalizability and fairness. The deployment architecture leveraged Microsoft Azure to facilitate a scalable, secure, and efficient operational framework.

Patient Perspectives on AI for Mental Health Care: Cross-Sectional Survey Study.

Background: The application of artificial intelligence (AI) to health and health care is rapidly increasing. Several studies have assessed the attitudes of health professionals, but far fewer studies have explored the perspectives of patients or the general public. Studies investigating patient perspectives have focused on somatic issues, including those related to radiology, perinatal health, and general applications.

Preparing for the bedside-optimizing a postpartum depression risk prediction model for clinical implementation in a health system.

Objective: We developed and externally validated a machine-learning model to predict postpartum depression (PPD) using data from electronic health records (EHRs). Effort is under way to implement the PPD prediction model within the EHR system for clinical decision support. We describe the pre-implementation evaluation process that considered model performance, fairness, and clinical appropriateness.

Visualizing machine learning-based predictions of postpartum depression risk for lay audiences.

Objectives: To determine if different formats for conveying machine learning (ML)-derived postpartum depression risks impact patient classification of recommended actions (primary outcome) and intention to seek care, perceived risk, trust, and preferences (secondary outcomes).

Materials And Methods: We recruited English-speaking females of childbearing age (18-45 years) using an online survey platform. We created 2 exposure variables (presentation format and risk severity), each with 4 levels, manipulated within-subject.

Women's perspectives on the use of artificial intelligence (AI)-based technologies in mental healthcare.

This study aimed to evaluate women's attitudes towards artificial intelligence (AI)-based technologies used in mental health care. We conducted a cross-sectional, online survey of U.S.

Identifying urban built environment factors in pregnancy care and maternal mental health outcomes.

Backgrounds: Risk factors related to the built environment have been associated with women's mental health and preventive care. This study sought to identify built environment factors that are associated with variations in prenatal care and subsequent pregnancy-related outcomes in an urban setting.

Methods: In a retrospective observational study, we characterized the types and frequency of prenatal care events that are associated with the various built environment factors of the patients' residing neighborhoods.

Development and validation of a machine learning algorithm for predicting the risk of postpartum depression among pregnant women.

Objective: There is a scarcity in tools to predict postpartum depression (PPD). We propose a machine learning framework for PPD risk prediction using data extracted from electronic health records (EHRs).

Methods: Two EHR datasets containing data on 15,197 women from 2015 to 2018 at a single site, and 53,972 women from 2004 to 2017 at multiple sites were used as development and validation sets, respectively, to construct the PPD risk prediction model.