Authors - Zarak Khan, Jiatong Yang, Rimshah Jawad, Ivania Martinez, Md Mozammel Hoque, Xinyi Zhao, Jim Samuel Abstract - The birth of a child brings immense joy to a mother’s life. However, the reality can be different for mothers experiencing Postpartum Depression (PPD). According to the World Health Organization (WHO), around 13% of women experience postpartum mental health disorders, with rates rising to nearly 20% in developing countries. PPD is a condition that affects many women worldwide, but because of the social stigma and the lack of accessible mental health support, it often goes undiagnosed or untreated. This paper presents MOMCare, a chatbot designed to support mothers navigating the challenges of PPD. MOMCare has a retrieval-augmented architecture with an end-to-end pipeline from data preprocessing to response generation. It employs hybrid classification, a dual embedding system, a dual verification guardrail, and a medical domain-specific reranking mechanism to generate empathetic and relevant PPD responses. This refined design of Retrieval Augmented Generation (RAG) ensures fast and factual response by reducing noise in retrieval and providing abundant context to gpt-3.5-turbo. MOMCare was evaluated using both automated and human metrics. Results show strong performance in both evaluations, which underlines the potential for chatbot interventions in the postpartum mental health domain. This system is robust enough to take new data and create a conversation generation pipeline that includes new information. Expanding the knowledge base using the conversation history with the users is also in development. The MOMCare chatbot and its features were built on sound ethical principles of healthcare and Artificial Intelligence (AI) and present a strong design emphasis on safety and fairness.
