Identifying the underlying factors associated with antidepressant drug discontinuation: Content analysis of patients’ drug reviews (Preprint)

Abstract

The rate of antidepressant prescriptions is globally increasing. A large portion of patients stop their medications which could lead to many side effects including relapse, and anxiety.

The aim of this was to develop a drug-continuity prediction model and identify the factors associated with drug-continuity using online patient forums.

We retrieved 982 antidepressant drug reviews from the online patient’s forum AskaPatient.com. We followed the Analytical Framework Method to extract structured data from unstructured data. Using the structured data, we examined the factors associated with antidepressant discontinuity and developed a predictive model using multiple machine learning techniques.

We tested multiple machine learning techniques which resulted in different performances ranging from accuracy of 65% to 82%. We found that Radom Forest algorithm provides the highest prediction method with 82% Accuracy, 78% Precision, 88.03% Recall, and 84.2% F1-Score. The factors associated with drug discontinuity the most were; withdrawal symptoms, effectiveness-ineffectiveness, perceived-distress-adverse drug reaction, rating, and perceived-distress related to withdrawal symptoms.

Although the nature of data available at online forums differ from data collected through surveys, we found that online patients forum can be a valuable source of data for drug-continuity prediction and understanding patients experience. The factors identified through our techniques were consistent with the findings of prior studies that used surveys.