Suicidal Ideation Detection: A Review of Machine Learning Methods and Applications

Abstract: Suicide is a critical issue in modern society. Early detection and prevention of suicide attempts should be addressed to save people's life. Current suicidal ideation detection methods include clinical methods based on the interaction between social workers or experts and the targeted individuals and machine learning techniques with feature engineering or deep learning for automatic detection based on online social contents. This paper is the first survey that comprehensively introduces and discusses the methods from these categories. Domain-specific applications of suicidal ideation detection are reviewed according to their data sources, i.e., questionnaires, electronic health records, suicide notes, and online user content. Several specific tasks and datasets are introduced and summarized to facilitate further research. Finally, we summarize the limitations of current work and provide an outlook of further research directions.

• The paper presents a comprehensive review of ML methods for suicidal ideation detection, categorizing approaches like clinical, content analysis, feature engineering, and deep learning.
• It highlights strong detection results, with studies reporting up to 85% accuracy in identifying suicidal signals from diverse data sources.
• It outlines future research directions including enhancing model interpretability, incorporating commonsense reasoning, and addressing ethical and data challenges.

Suicidal Ideation Detection: A Review of Machine Learning Methods and Applications

This paper presents a comprehensive survey on the detection of suicidal ideation using machine learning techniques, highlighting the increasing importance of addressing suicide prevention effectively. The authors examine various approaches categorized under clinical methods, content analysis, feature engineering, and deep learning, applied across distinct domains such as questionnaires, electronic health records (EHR), suicide notes, and online user-generated content.

Overview of Machine Learning Methods

The paper methodically categorizes existing methodologies into several classes, each offering distinct advantages and insights into suicidal ideation detection:

1. Clinical Methods: These include face-to-face interactions and self-reports. However, the potential of machine learning to automate these processes and improve efficiency is emphasized.
2. Content Analysis: Utilizes lexicon-based filtering and topic modeling on user-generated text to extract sentiment and uncover linguistic patterns that might indicate suicidal ideation.
3. Feature Engineering: This technique involves selecting pertinent features from tabular and text data. The paper reports on research employing classical feature sets like TF-IDF and syntactic attributes to train machine learning classifiers such as SVM and neural networks.
4. Deep Learning: Focuses on using models like CNNs, RNNs, and BERT to automatically learn and predict suicidal risks from text data. The growing use of advanced paradigms like transfer learning and knowledge incorporation is highlighted as promising future directions.

Strong Results and Claims

The survey discusses various studies boasting considerable success rates for detecting suicidal ideation using feature engineering and deep learning approaches. For instance, a detection accuracy upwards of 85% is reported in several studies employing CNN and topic modeling.

Implications and Future Research Directions

This survey addresses the practical implications of employing machine learning techniques for suicide prevention. It underscores the significance of early detection, which not only saves lives but also reduces the societal burden caused by suicide. However, the paper also notes the ethical and privacy concerns that arise from employing AI to predict and potentially intervene in suicidal crises.

Theoretical implications include advancing AI methodologies to better grasp suicidal intentions, and integrating psychological models to provide a more nuanced understanding of suicidal ideation.

Looking forward, the authors propose several research directions:

• Enhancing model interpretability and accuracy by incorporating commonsense reasoning via external knowledge bases.
• Exploring temporal modeling of suicidal ideation for early detection and intervention.
• Development of proactive conversational AI that can engage with users exhibiting signs of suicidality.
• Addressing data deficiency and annotation biases to create robust, comprehensive datasets for model training.

Conclusion

The paper provides a critical resource for researchers aiming to tackle suicide prevention using machine learning. While challenges remain, particularly around data availability and ethical considerations, machine learning holds significant promise for the future of mental health care. The paper calls for a global effort in advancing and integrating AI into practical applications that can benefit individuals and communities at risk of suicide, making this an area ripe for continued research and development.