Natural Language Processing Framework for Lowering the Incidence of Public Health Disorders

Abstract

Using Digital Technology (DT), which includes collecting, analyzing, and using data from different digital devices, can lower the number of diseases people get and improve their mental health. Digital Health Interventions (DHIs) can help with certain conditions quickly and successfully in a way that is both cost-effective and based on science. Natural Language Processing (NLP) gives ways to analyze writing, better understand interventions' effects, and make therapy decisions. This study aimed to develop a way to use technology to make it easier to automatically analyze both types of written data that are common in DHIs. This method creates textual traits and allows statistical models to predict goal factors like user involvement, condition change, and treatment outcomes. The study supports locating together outcome-optimizing teams that use data from various sources. The research uses complex data analysis and new methods to develop techniques and approaches that make prevention and therapy measures more widely available, accepted, used, and effective.