AI for Well-Being (AWB)

This milestone focuses on establishing the foundation of our mental health detection system by creating a comprehensive and well-structured dataset. The work involves gathering relevant mental health-related text data from multiple sources and organizing it into a format suitable for machine learning applications. This phase is crucial as it will determine the quality and effectiveness of our final model's ability to detect various mental health conditions.

- Collection of labeled text datasets from various sources containing mental health-related content with specific mental health conditions (e.g. depression anxiety eating disorders etc.) - Implement strict anonymization protocols to remove all usernames, personal identifiers, and sensitive information from collected posts, ensuring user privacy protection - Integration of multiple datasets into a unified format with consistent labeling for different mental health conditions - Dataset organization and splitting including: 1. Combining multiple dataset sources into a single coherent file 2. Creating balanced training (75%) validation (15%) and test (15%) sets 3. Ensuring proper class distribution across all splits for each condition (eating disorder anxiety depression etc.)

$15,000 USD

- Clean, properly labeled dataset encompassing various mental health conditions - Balanced dataset splits (training/validation/test) with appropriate representation across all mental health categories

This milestone addresses the critical data preparation phase focusing on transforming raw text data into a clean standardized format suitable for machine learning model training. The preprocessing phase ensures that our model can effectively learn from the text data by removing noise and standardizing the input format while preserving the essential information needed for mental health condition detection.

- Implementation of comprehensive text cleaning procedures: 1. Text normalization through lowercasing to ensure consistent handling (e.g. "I'm worried" and "im worried" are treated the same) 2. Special character and non-alphanumeric content removal including punctuation marks and extra spaces 3. Social media user handle removal as usernames don't provide information about sentiment/mental health issues 4. Tokenization implementation based on model requirements 5. Stopword removal (e.g. "the" "is" "in") for dimensionality reduction 6. Emoji and slang conversion to textual representations 7. Spell checking and word normalization (e.g. "u" to "you" "lol" to "laughing") to ensure variations aren't treated as different words - Quality assurance checks on cleaned data - Documentation of preprocessing steps and procedures

$20,000 USD

- Fully preprocessed and tokenized text data ready for model training - Consistent formatting across all dataset entries

This milestone encompasses the core machine learning phase of the project focusing on developing and optimizing a BERT-based model for mental health condition detection. This phase involves selecting and fine-tuning appropriate deep learning architectures implementing effective training strategies and ensuring the model achieves high performance across all mental health categories.

- Implementation of model architecture and training pipeline including selection and configuration of appropriate BERT-based model (e.g. ModernBERT or BERT) and implementation of cross-entropy loss function for multi-class or binary classification tasks - Comprehensive hyperparameter optimization through grid search or random search encompassing learning rate batch size number of epochs dropout rate and other relevant parameters followed by model training execution with the optimized configuration - Development of a robust evaluation framework with multiple metrics (accuracy precision recall and F1-score) with particular emphasis on analyzing performance for underrepresented mental health conditions (eating disorder anxiety etc.) - Complete documentation of model architecture training process and performance analysis including detailed reporting of optimization decisions and their impacts

$10,000 USD

- Trained model demonstrating strong performance in classifying input text across all mental health categories (depression, anxiety, eating disorder, etc.), with particular attention to accuracy in minority classes - Comprehensive performance report including F1-score, precision, and recall metrics across all mental health conditions - Complete model deployment package including saved model weights, configuration files, and documentation for reproduction

This milestone focuses on making the trained mental health detection model accessible and operational within the SingularityNET ecosystem. The deployment phase ensures that the model can be effectively utilized by users with proper integration into the platform's infrastructure and comprehensive testing to guarantee reliable performance.

- Ensure no user data is stored or retained after processing. By implementing strict data handling protocols that guarantee all user inputs are discarded immediately after the service processes them, with no persistent storage or logging of any user data at any stage. - Verify diverse representation in the training dataset across demographics, ensuring that the dataset includes a wide range of groups to promote fairness and reduce bias, while reflecting the varied nature of real-world data for better accuracy and inclusivity in model predictions. - Complete service configuration setup and deployment of the mental health issue detection service within the SingularityNET platform, including all necessary platform-specific adaptations and optimizations - Publication of the service to the SingularityNET marketplace, ensuring proper accessibility for both users and other AI agents - Execution of comprehensive testing including functionality verification, performance validation, reliability testing, and integration testing with other platform services to ensure robust operation - Development of complete documentation and support materials for service usage and maintenance

$5,000 USD

- Successful deployment and accessibility verification on SingularityNET - Complete resolution of all testing-identified issues - Smooth operation within the SingularityNET environment