Multimodal Suicide Tendency Detection Model: An AI Mental Health Screening Tool Fusing Text and Audio

This project was published by developer pranjal-2218 on GitHub (link: https://github.com/pranjal-2218/Multimodal-suicide-detection). It builds a multimodal machine learning model that fuses text and audio data to identify suicide tendencies, with a test accuracy of 93%. This article will discuss aspects such as background, technical methods, evaluation, application scenarios, and ethical considerations.

Traditional suicide tendency screening relies on clinical interviews and questionnaires, which have problems such as strong subjectivity, poor timeliness, and limited coverage. Single-modal data (e.g., text only or audio only) cannot fully capture mental states—text may be modified, and voice emotional cues are easily overlooked. Multimodal fusion technology provides new possibilities for improving recognition accuracy.

The core goal of the project is to build a suicide tendency detection model that processes both text and audio inputs simultaneously. The repository contains three key files: final_ai_model_.ipynb (model training and evaluation code), final_suicidal_dataset.csv (training and test dataset), and final_suicidal_report.pdf (project report). The dataset should include text samples (e.g., social media posts, interview records), corresponding audio, and professionally labeled risk level tags.

The core innovation of the model lies in fusing two information sources:

1. Text Modality: Extract semantic features, sentiment polarity, keywords, etc.
2. Audio Modality: Extract acoustic features such as pitch, speech rate, pause patterns, and energy distribution. A typical model structure includes: a text encoder (e.g., BERT/RoBERTa), an audio encoder, a fusion layer (concatenation or attention weighting), and a classifier (binary classification: with/without suicide tendency).

The model achieved an accuracy rate of 93% on the test set, but it should be noted that accuracy is only one of the evaluation metrics. In practical applications, attention should be paid to precision, recall, F1 score, especially the false negative rate (the cost of missing high-risk individuals is high).

This technology can be applied in:

1. Online Psychological Counseling Platforms: Real-time analysis of users' text/voice to help counselors prioritize high-risk cases.
2. Social Media Monitoring: Identify users in need under privacy protection and provide resource links.
3. Clinical Auxiliary Diagnosis: Assist doctors in screening large populations, improving efficiency and consistency.

Privacy Protection: Strictly comply with data regulations, ensure user informed consent, and adopt encrypted storage and anonymization processing. Limitations:

• Cultural differences affect generalization ability;
• Individual differences lead to failure to capture all risk signals;
• A 93% accuracy rate still has a 7% misjudgment rate, so it cannot replace professional judgment. Ethical Red Line: The tool is only for auxiliary purposes and cannot replace professional assessment. Avoid harm caused by false positives or false negatives.

This project demonstrates the potential of multimodal machine learning in the field of mental health, and the 93% accuracy rate provides a technical solution for automated screening. Future directions include: expanding the dataset to improve generalization ability, introducing more modalities (video/physiological signals), developing fine-grained risk grading models, and establishing ethical review and manual review mechanisms. Technical innovation should be balanced with ethical responsibility.