AI Mental Health Assistant

The "AI Mental Health Assistant" utilizes multiple collaborating agents with continuous memory of user interactions and complex workflows to deliver personalized support for mental health challenges.

Introduction

The "AI Mental Health Assistant" utilizes multiple collaborating agents with continuous memory of user interactions and complex workflows to deliver personalized support for mental health challenges.

Overview

Here is an overview of how it works:

Basic Project Operation Logic

Data

The template consists of 6 core tables designed to collect and store essential user data:

• Account: Default system table enhanced with fields for user body metrics (e.g., height, weight).

• Medical History: Stores users' previous medical history.

• Medication Records: Documents users' past medication history.

• Journal: Tracks users' daily journal entries.

• Crisis Plan: Contains professional medical knowledge for AI retrieval; additional professional data can be inserted as needed.

• Conversation Summary: Stores summaries of each conversation to provide the AI with memory.

UI

The template features six main pages:

• 【Login/Register】: Users can log in or sign up using email as the default method, with options for additional methods configurable in settings.

• 【Basic Info】: User profile page displaying basic metrics and medical history. Users can update their personal data here.

• 【Home】: The homepage of the app, aggregating entries from all other pages.

• 【Journal】: Users can update their journal and modify its status. If marked as private, the AI agent will not retrieve the content.

• 【Chat】: A chat interface where users can interact with the AI to receive personalized support.

AI Configuration

• Multi-Agent Architecture:

  • Design multiple AI agents, each responsible for specific tasks (e.g., summarization, symptom analysis, casual conversation).

  • Configure agents to collaborate seamlessly, optimizing the user experience and ensuring accurate responses.

• Retrieval-Augmented Generation (RAG): Implement RAG to pull relevant information from user data and previous interactions, ensuring personalized responses.

Workflow

1. User Input & Data Retrieval:

   • Users submit inquiries regarding their health or emotional state.

   • The system retrieves relevant medical history and emotional feedback from the structured database.

2. AI-Driven Crisis Prediction:

   • Utilize vector search to match user inputs with existing intervention strategies.

   • Cross-reference symptoms with predefined crisis intervention plans.

3. Generating Personalized Recommendations:

   • Analyze user conditions in the context of their history to formulate customized intervention strategies.

4. Output & User Profile Optimization:

   • Provide suggestions or adjustments to the user's personal intervention plan.

   • Update the user’s health profile to ensure continuous improvement of future recommendations.

5. Actionflows for Automation:

   • Implement Actionflows to automate backend processes, ensuring real-time updates and efficient management of AI agent collaboration.

If you want to learn more about how this template is built, check our detailed breakdown blog.