TOWARDS SMARTER MENTAL HEALTH CARE: MACHINE LEARNING-BASED SUICIDAL IDEATION DETECTION TECHNIQUES

Abstract

The need for effective techniques to identify and intervene in cases of suicidal thoughts in order to avoid self-harm and fatalities has grown in recent years, making this a critical issue in mental health worldwide. Although self-reporting and traditional clinical examinations have their uses, they are often constrained by issues including underreporting, stigma, and a lack of real-time monitoring. The development of AI has opened up exciting new possibilities for the field of mental health. One such area is machine learning (ML), which can automatically identify suicidal thoughts from a variety of sources, including clinical notes, social media posts, speech patterns, and physiological signals. Modern machine learning (ML) approaches to detecting suicidal thoughts are examined in this study. These approaches include supervised and unsupervised learning, deep learning architectures, and NLP frameworks. Ethical concerns related to algorithmic bias and patient privacy, as well as feature extraction, model interpretability, and data preparation techniques, are given special attention. The abstract goes on to discuss current difficulties, potential future research topics, and practical uses of intelligent systems in clinical decision-making. This research highlights the potential of machine learning to help prevent suicide via proactive, scalable, and personalised ways by integrating mental health care with computational intelligence.