Machine Learning-based Suicide Risk Text Detection System: FastAPI Service Implementation and Application

This article introduces an open-source suicide risk detection project that uses machine learning technology to classify text messages, identify potential suicide risks, and provide a FastAPI service interface to support real-time intervention. The system achieves an accuracy rate of 93.33%, offering a technical solution for mental health monitoring and crisis intervention.

Mental health issues are receiving increasing attention worldwide, and suicide prevention is a top priority among them. According to the World Health Organization, nearly 800,000 people die by suicide each year. In the digital age, platforms like social media and instant messaging have become important channels for people to express their emotions, which may contain distress signals. How to use artificial intelligence technology to identify these signals in a timely manner and gain time for crisis intervention has become a research direction with important social value.

Machine Learning Model

The project uses machine learning technology for text classification, and the model achieves an accuracy rate of 93.33% on the test set. Based on common practices in text classification, traditional machine learning methods (such as SVM, Random Forest), deep learning models (such as LSTM, BERT), or ensemble methods may be used.

FastAPI Service Framework

The project chooses FastAPI as the web service framework, which has advantages such as high performance (asynchronous programming), type safety (data validation), automatic documentation (OpenAPI/Swagger), and easy deployment (Docker support).

The system has an accuracy rate of 93.33% and can be integrated into various scenarios:

1. Social media monitoring platforms: 24/7 scanning of risky content
2. Online psychological counseling systems: Assisting counselors in locating high-risk conversations
3. Crisis hotline systems: Real-time analysis of callers' text descriptions
4. School/enterprise early warning: Detecting abnormal expressions of employees or students

These applications improve intervention efficiency and provide data support for research.

Technical Challenges

• Data acquisition and annotation: Involves privacy ethics and is difficult
• Class imbalance: Few high-risk samples, need to handle sample imbalance
• Language diversity: Expression styles vary due to cultural and other factors
• Context understanding: Single-sentence analysis is prone to misjudgment, need to consider conversation history

Ethical Considerations

• Privacy protection: Comply with regulations such as GDPR and HIPAA
• Misjudgment risk: Balance false positives (resource waste) and false negatives (delayed rescue)
• Human intervention: Technology assists professional decisions, not fully automated
• Transparency: Provide explanations for judgment basis

1. Model fine-tuning: Use domain-specific data to fine-tune and improve performance
2. Threshold adjustment: Balance recall and precision according to needs
3. Integration of feedback: Collect verification results for iterative optimization
4. Multilingual support: Expand to more languages
5. Security reinforcement: Strengthen API protection to prevent data leakage

Suicide risk detection is an important application of NLP in the field of mental health, and this project demonstrates the value of technology in building practical systems. However, technology is only a tool; real change requires social attention and professional help. We look forward to more open-source projects transforming technology into social value and building a healthier digital society.