Identifying the underlying factors associated with antidepressant drug discontinuation: content analysis of patients’ drug reviews

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 Random 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 perceiveddistress 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.

KEYWORDS:

• Antidepressive agents
• data mining
• attitude
• chronic disease
• depression
• social media
• internet
• patient-centered care
• medication adherence
• perception

Abbreviations

ADR: adverse drug reaction

ADR-PD: perceived distress from ADRs

WD: withdrawal symptom

ADR-WD: perceived distress from WDs

EF-INF: Ineffectiveness and Effectiveness in the health condition

KN: Lack of knowledge

DXD: decision for discontinuation because patient forgot to use the medication

Logistic Regression (LR)

Random Forest (RF)

Gradient Boosting Machine (GBM)

K Neighbor Classifier (k-nn)

Support Vector Machine (SVMs)

Deep Neural Network (DNN).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.