Text Mining Approach To Predict Non-Adherence

Abstract

Companies operating patient support programs for chronic diseases have been dedicated to enhancing treatment adherence by utilizing data from various interventions of the programs. The purpose of this paper is to examine whether the textual patient notes recorded by program coordinators can be beneficial to predict non-adherence and provide useful insights. In this paper we show work in processing and analyzing over 20,000 patient notes corresponding to 1313 Psoriasis patients using statistical analysis and several NLP methods, such as term representation, sentiment analysis and topic modelling. To build predictive models, Support Vector Machine (SVM), Random Forest (RF) and Logistic Regression (LR) are tested with different feature subsets. The best performing model is SVM with 93% accuracy and 91% recall of non-adherent. Additionally, we also present patterns to differentiate non-adherent and adherent patients in terms of completion efficiency of call objectives and uncontactable problem. Accordingly, high-risk patients can be targeted to take interventions.