Predicting Patient Satisfaction With Medications for Treating Opioid Use Disorder: Case Study Applying Natural Language Processing to Reviews of Methadone and Buprenorphine/Naloxone on Health-Related Social Media.
Samaneh OmranianMaryam ZolnooriMing HuangCeleste Campos-CastilloSusan McRoyPublished in: JMIR infodemiology (2023)
Assessment of patients' satisfaction with opioid dependency treatment medication can be predicted using automated text analysis. Adding biomedical concepts such as symptoms, drug name, and illness, along with the duration of treatment and topic models, had the most benefits for improving the prediction performance of the Elastic Net model compared to other models. Some of the factors associated with patient satisfaction overlap with domains covered in medication satisfaction scales (eg, side effects) and qualitative patient reports (eg, doctors' visits), while others (insurance) are overlooked, thereby underscoring the value added from processing text on online health forums to better understand patient adherence.
Keyphrases
- patient satisfaction
- social media
- health information
- healthcare
- adverse drug
- case report
- ejection fraction
- public health
- newly diagnosed
- mental health
- type diabetes
- chronic pain
- systematic review
- emergency department
- skeletal muscle
- high throughput
- sleep quality
- replacement therapy
- chronic kidney disease
- randomized controlled trial
- depressive symptoms
- insulin resistance
- adipose tissue
- physical activity