Approaches to Text Mining for Analyzing Treatment Plan of Quit Smoking with Free-text Medical Records (Preprint)
BACKGROUND Smoking is a complex behavior associated with multiple factors such as personality, environment, genetics, and emotions. Text data is a rich source of information. However, pure text data requires substantial human resources and time to extract and apply the information, resulting in many details not being discovered and used. OBJECTIVE This study proposes a novel approach that explores a text mining flow to capture the behavior of smokers quitting tobacco from their free-text medical records. More importantly, the paper explores the impact of these changes on smokers. The goal is to help smokers quit smoking. Therefore, the paper develops an algorithm for analyzing smoking cessation treatment plans documented in free-text medical records. METHODS The approach involves the development of an information extraction flow that uses a combination of data mining techniques, including text mining. It can be used not only to help others quit smoking but also for other medical records with similar data elements. RESULTS In the paper, the most visible areas for the medical application of text mining are the integration and transfer of advances made in basic sciences, as well as a better understanding of the processes involved in smoking cessation. CONCLUSIONS Text mining may also be useful for supporting decision-making processes associated with smoking cessation.