AI-Driven Prediction of Suicidal Attempts from Healthcare Data: A Novel Framework

Abstract

Suicide is a significant public health concern. This research develops a novel framework leveraging artificial intelligence (AI) and readily available healthcare data to predict suicidal attempts. The framework integrates various data sources, employs machine learning algorithms for risk prediction, and incorporates explainability techniques to ensure transparency and clinical utility. Our preliminary findings suggest the potential for improved accuracy in identifying individuals at high risk, enabling timely intervention and potentially saving lives. The framework emphasizes responsible AI development, balancing predictive performance with ethical considerations. Further validation and refinement are necessary before clinical deployment.

Introduction

Suicide rates remain alarmingly high globally, placing a considerable burden on healthcare systems and society. Early identification of individuals at risk is crucial for effective intervention. Traditional methods rely heavily on clinical judgment and structured interviews, which can be subjective and resource-intensive. The availability of large-scale healthcare datasets, coupled with advancements in AI, presents an opportunity to develop more accurate and efficient prediction models. However, challenges remain in addressing data privacy, ensuring model explainability, and mitigating potential biases within the data. This research aims to address these challenges by developing a robust and ethically sound framework for predicting suicidal attempts.

Technical Features

• Uses AI models (Random Forest, XGBoost, or Deep Neural Networks)
• Trained on anonymized Electronic Health Record (EHR) data
• Feature extraction from clinical, behavioral, and demographic data
• Integrated SHAP (SHapley Additive exPlanations) for model transparency
• Ethically balanced framework ensuring privacy and fairness