Overview
A renowned non-profit organization dedicated to anxiety awareness required an advanced solution to predict mental health conditions by analyzing behaviors from videos and social media posts. Traditional methods of mental health assessment are often time-consuming and subjective. This research project aimed to leverage AI and machine learning technologies to provide a more objective, efficient, and scalable approach to mental health condition prediction.
Problems
1.Traditional mental health assessments are time-consuming and subjective, leading to potential inconsistencies.
2.There is a need for a scalable solution to analyze large volumes of data from videos and social media posts to predict mental health conditions.
3. The organization required a modern solution to streamline the mental health prediction process and improve early detection and intervention.
Key Requirements
1. Development of an AI-driven system to analyze behaviors from videos and social media posts.
2. Implementation of machine learning algorithms to predict mental health conditions based on behavioral data.
3. Integration of natural language processing (NLP) to analyze text from social media posts.
4. Real-time data processing and analysis for timely predictions.
5. Creation of a secure database to store and manage sensitive data.
6. Ensuring the highest level of data security and privacy protection for users' information.
Maintenance and Support: The admin interface provides tools for monitoring performance, updates, and technical support.
Responsive Design: The web app and admin interface are compatible across various devices and screen sizes.
Our Solutions
Design Process
This project involved teamwork between UI and UX designers to create an innovative solution. Our business and growth team stayed in constant communication, providing valuable insights throughout the process.
Results
- Improved Efficiency: The AI-driven system reduced the time required for mental health assessments by 50%, allowing for quicker interventions.
- Enhanced Accuracy: Achieved an 85% accuracy rate in predicting mental health conditions, providing more reliable assessments.
- Scalability: Â The solution scaled to analyze large volumes of data, handling thousands of videos and social media posts daily.
- Data Security: Implemented advanced data security measures, ensuring the privacy and protection of users’ information.
- Early Detection and Intervention: The system enabled early detection of mental health conditions, facilitating timely support and intervention.
Harnessing AI to Predict Mental Health Conditions from Behaviors & Social Media
Current Status
The AI-driven mental health condition prediction system is fully operational, providing the Anxiety Awareness Organization with advanced tools to analyze behaviors and predict mental health conditions. The system has significantly improved the efficiency, accuracy, and scalability of mental health assessments, supporting the organization’s mission to raise awareness and provide timely interventions.
Tech Stack
