Using text analysis for chronic disease management
The diagnosis, management, and treatment of chronic diseases (e.g., diabetes, chronic obstructive pulmonary diseases, and heart failure) have traditionally been focused on longitudinal histories and physical examinations as primary tools of assessment, and augmented by laboratory testing and imaging. Equally important to history taking and physical examinations is the objective assessments and understanding of the contribution of the patients' states of mind to their disease states. This is historically only documented qualitatively but highly challenging to measure quantitatively. However, recent advances in data science techniques such as natural language processing are providing new opportunities. Speech and text analysis is an emerging strategy to carry out analysis of cognition, sentiments, physical symptoms and social influences for such potential quantification. Thus, this project seeks to integrate speech and text analysis into the longitudinal management of chronic diseases to maintain optimal stability, support recovery and detect deterioration. Furthermore, the project will analyze synergistic measurements of speech and text analysis with physiologic data captured by wearables and sensors used in chronic disease management to gauge the states of stability of patients’ chronic diseases.