The current mental health problem is a major public health concern that needs a comprehensive solution and cannot be handled by traditional services alone. The World Health Organization estimates that every year, 700,000 individuals die by suicide worldwide. In the UK, particularly among young women, the proportion of people who met diagnostic standards for mental illnesses or self-harming was rising even before the COVID-19 epidemic. With more people experiencing heightened anxiety and mental health pressure, and health services unable to keep up with demand, the COVID-19 epidemic has only made this concerning situation worse. To deal with ever-increasing mental health problems worldwide, NLP proves a boon to medical practitioners. BioBERT and Med-BERT are two examples of how NLP has been applied in the field of medicine and healthcare. NLP has also been used in the field of mental health to predict suicidal thoughts, analyze PTSD, predict psychosis and other diseases, create fake mental health records, and conduct motivational interviewing.
This book focuses on recent developments in behavioral-based natural language processing for mental health that explores methodologies and various aspects of mathematical analysis covering various approaches such as behavior signal processing from speech and language, speech analysis, clinical natural language processing, rule based chatbot approaches etc.