Social Isolation

Social isolation, encompassing both physical and social disconnect, is a significant research area focusing on its detection, mitigation, and impact across diverse domains. Current research employs various machine learning approaches, including neural networks, isolation forests, and rule-based systems, to identify instances of isolation from diverse data sources like telemetry, clinical notes, and sensor readings. These efforts aim to improve anomaly detection, enhance model robustness against adversarial attacks, and ultimately contribute to better understanding and addressing the negative consequences of social isolation on individual and societal well-being. The development of more accurate and efficient methods for identifying and mitigating social isolation holds significant promise for improving healthcare, enhancing technological systems, and promoting overall societal health.