Despite the fact that user-generated data are widely used in medical informatics in general and for revealing side-effects of various pharmaceuticals in particular, recent studies have focused merely on methods of extracting information on side effects from unstructured or semi-structured reviews of specific medications without linking side effects to any outcomes.
In this study we demonstrate how user-generated online content on side effects experienced by patients while taking a pharmaceutical product can be used to do research after the drug has been introduced to the market, thus allowing to complement the results of official clinical studies and market research. In particular, we concentrate on revealing the contribution of various side effects to reported customer satisfaction with Tamiflu, a popular antiviral drug.
Publicly available data from an online platform with reviews from patients are used as an input to the analysis that applies statistical and machine learning methods (multivariate logit models and classification trees) to investigate the relationships of side effects to demographic characteristics and to the overall satisfaction with the medication.
We prioritized side effects of Tamiflu based on the significance of their association with patient’s ratings published at one of the well-known drug discussion forums. Among all types of side effects used in our study, the neuropsychiatric symptoms and body pain are the most influential, followed by skin problems. Specific combinations of side-effects that are associated with low satisfaction were detected.
The proposed analytical approach can help pharmaceutical companies to improve their products and/or medical guidelines associated with their products and figure out fighting which adverse effects should be given a priority from the customer satisfaction perspective.