Abstract
Postpartum depression (PPD) affects 10-15% of individuals annually, yet early identification and treatment remains challenging. We introduce ClinPreAI, a novel agentic AI system that autonomously designs, implements, and evaluates machine learning solutions for PPD risk prediction using multimodal electronic health record data. We analyzed data from 4,161 pregnant individuals hospitalized prior to delivery for medical or obstetrical complications at Texas Children's Hospital (2012-2025), extracting 27 structured clinical variables and social worker notes. The primary outcome was Edinburgh Postnatal Depression Scale (EPDS) score ≥10 (31.0% prevalence) within 6 months after delivery, indicating clinically significant depressive symptoms. ClinPreAI operates through five specialized modules that iteratively refine predictive models through autonomous experimentation. ClinPreAI demonstrated strong performance across modalities. On structured data, it achieved F1: 0.68 ± 0.03, outperforming traditional AutoML (F1: 0.64 ± 0.02) and commercial solutions (AWS Canvas F1: 0.54-0.55). On multimodal data, ClinPreAI achieved F1: 0.65 ± 0.04, matching custom LLM-XGBoost (F1: 0.65 ± 0.01) and outperforming zero-shot models (Claude Opus F1: 0.51-0.52). This represents the first application of agentic AI to perinatal mental health prediction. Our results demonstrate that autonomous AI agents can democratize sophisticated predictive modeling in clinical settings, which is particularly valuable where domain experts lack ML training. By automating experimentation and debugging, agentic systems lower barriers to developing robust clinical prediction tools while maintaining interpretability.