Machine Learning for Combating Mental Health Stigma

Abstract

Stigma around mental health hinders care through discrimination, misconceptions, and shame. Machine learning (ML) offers data-driven solutions to address this, using sentiment analysis and NLP to analyze public attitudes, identify stigmatizing language, and guide awareness campaigns. In healthcare, ML reduces biases, enhances patient interactions, and fosters inclusivity. Personalized education via AI combats misinformation, while advocacy campaigns leverage ML to assess impact. By addressing ethical concerns like bias and privacy, ML can transform mental health stigma reduction.