Mental_Health_Analysis_System

## Executive Summary & Business Understanding

Digital mental health platforms receive thousands of unstructured text messages, self-reflection journals, survey responses, and support requests every day.

### Current Pain Points:

1. **Manual Screening Bottlenecks:** Clinical staff and support agents cannot review high volumes of incoming text in real time.

2. **Delayed Intervention in High-Risk Situations:** Critical cases (such as suicidal ideation or acute crisis) risk being buried under standard support queues.

3. **Staff Burnout:** Repetitive manual triage overwhelms mental wellness professionals.

4. **Lack of Continuous Insights:** Unstructured text data contains valuable clinical patterns that remain unquantified without automated NLP intelligence.

### Core Business Objectives:

* **Automated Screening:** Instantly classify unstructured submissions into 7 diagnostic categories (`Normal`, `Depression`, `Suicidal`, `Anxiety`, `Stress`, `Bi-Polar`, `Personality Disorder`).

* **Risk Stratification:** Triage cases into actionable risk tiers (`Low`, `Moderate`, `High`, `Urgent / Critical`).

* **Immediate Escalation:** Trigger automated real-time safety alerts and crisis hotline interventions for critical scores.

* **API Integration:** Provide decoupled, scalable RESTful API endpoints for web, mobile, and chatbot platforms.