AI-Powered PTSD Detection

Advanced sentiment analysis meets machine learning for mental health insights

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Understanding PTSD

What is PTSD?

Post-Traumatic Stress Disorder (PTSD) is a mental health condition triggered by experiencing or witnessing a terrifying event. It affects millions worldwide and can significantly impact daily functioning, relationships, and quality of life.

Common Symptoms
  • Intrusive memories and flashbacks
  • Avoidance of trauma-related stimuli
  • Negative changes in mood and cognition
  • Hyperarousal and reactivity
  • Sleep disturbances and nightmares
Why Early Detection Matters

Early identification of PTSD symptoms can lead to timely intervention and better treatment outcomes. Traditional screening methods rely on clinical interviews and questionnaires, which can be time-consuming and subjective.

Our Approach

This research tool uses advanced natural language processing and machine learning to analyze speech patterns and sentiment in text, providing objective insights that can support mental health professionals in their assessments.

Project Overview

Research Objectives

This application demonstrates how artificial intelligence can be applied to mental health screening through:

  • Text Analysis: Processing clinical interview transcripts and personal narratives
  • Sentiment Detection: Identifying emotional patterns using multiple NLP engines
  • Pattern Recognition: Machine learning models trained to recognize PTSD-related language patterns
  • Risk Assessment: Providing probability scores to support clinical decision-making
Academic Foundation

Built on established research in computational psychiatry, this tool incorporates methodologies from the DAIC-WOZ corpus and AVEC challenges, focusing on speech and language indicators of psychological distress.

Important Disclaimer

Research Tool Only: This application is designed for research and educational purposes.

Not a Diagnostic Tool: Results should not replace professional clinical assessment.

Privacy Protected: All processing occurs locally on your device.

Multi-Source Analysis

VADER & TextBlob sentiment engines with advanced feature engineering and 23-bin discretization

Ensemble Learning

Super Learner methodology combining Random Forest, Gradient Boosting, SVM, and LDA models

Privacy First

All processing runs locally - no data transmitted to external servers or stored remotely

Interactive Results

Real-time analysis with visualizations, export capabilities, and comprehensive reporting

How It Works

1. Text Input

Upload transcripts, interviews, or personal narratives for analysis

2. Sentiment Analysis

Extract emotional patterns using VADER and TextBlob NLP engines

3. AI Processing

Ensemble models analyze features and generate probability scores

4. Results & Insights

View predictions, sentiment analysis, and exportable reports

Ready to Explore AI-Assisted Mental Health Analysis?

Experience how machine learning can support mental health research and screening

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