Our research aims at addressing the needs of developing data analysis about communication in respect to care seeking and primary care, discovering how health expressions evolve along the personal growth and learning process, and how to solve the needs identified in respect to developing measuring the quality of life. We provide an overview of the development of a new research methodology exploiting machine learning for analyzing patient experience expressions to support personalized care and managing in everyday life. Our research relies on an online questionnaire in which the representatives of various population groups perform interpretation tasks. Dependencies between answers about the interpretation tasks and background information are analyzed with machine learning methods. The research creates new ways to interpret and address the meanings of language usage of different groups of patients and impaired carefully and distinctively as a part of everyday life and care events.Peer reviewe
