International Journal of Nursing Research

Objective To develop a multimodal, explainable AI–based system for dynamic prediction and stratified intervention of postpartum depression (PPD) and to evaluate its effectiveness in early identification and intervention within nursing settings. Methods In a quasi-randomized controlled study, 300 postpartum women within 6 hours after delivery were assigned to an observation group or a control group (150 each). The observation group received AI-assisted prediction and stratified care, while the control group received routine nursing. EPDS, HRV, sleep efficiency, and maternal–infant interaction abnormalities were assessed on postpartum days 7, 14, 28, and 42. Risk identification timeliness, EPDS changes, high-risk remission rates, and nursing workload were compared. Results The observation group showed faster risk identification (1.21±0.68 vs. 4.73±1.92 days, P<0.05), greater EPDS reduction (−5.62±3.14 vs. −2.13±2.87, P<0.05), and a higher remission rate (92.0% vs. 64.0%, P<0.05). Nursing workload decreased substantially, with reduced follow-up time, follow-up volume, and stress levels. The model achieved an AUC of 0.864 and an F1 score of 0.79, and explainable outputs improved nurses ' acceptance. Conclusion This multimodal explainable AI system enhances early PPD risk detection and intervention effectiveness, improves maternal emotional outcomes, and reduces nursing workload, demonstrating strong potential for clinical application.

Multimodal data; Postpartum depression; Nursing; Explainable AI; Risk prediction