Abstract
Epilepsy is a common neurological disorder worldwide and antiepileptic drug (AED) therapy is the cornerstone of its treatment. It has a laudable aim of achieving seizure freedom with minimal, if any, adverse drug reactions (ADRs). Too often, AED treatment is a long-lasting journey, in which ADRs have a crucial role in its administration. Therefore, from a pharmacovigilance perspective, detecting the ADRs of AEDs is a task of utmost importance. Typically, this task is accomplished by analyzing relevant data from spontaneous reporting systems. Despite their wide adoption for pharmacovigilance activities, the passiveness and high underreporting ratio associated with spontaneous reporting systems have encouraged the consideration of other data sources such as electronic health databases and pharmaceutical databases. Social media is the most recent alternative data source with many promising potentials to overcome the shortcomings of traditional data sources. Although in the literature some attempts have investigated the validity and utility of social media for ADR detection of different groups of drugs, none of them was dedicated to the ADRs of AEDs. Hence, this paper presents a novel investigation of the validity and utility of social media as an alternative data source for the detection of AED ADRs. To this end, a dataset of consumer reviews from two online health communities has been collected. The dataset is preprocessed; the unigram, bigram, and trigram are generated; and the ADRs of each AED are extracted with the aid of consumer health vocabulary and ADR lexicon. Three widely used measures, namely, proportional reporting ratio, reporting odds ratio, and information component, are used to measure the association between each ADR and AED. The resulting list of signaled ADRs for each AED is validated against a widely used ADR database, called Side Effect Resource, in terms of the precision of ADR detection. The validation results indicate the validity of online health community data for the detection of AED ADRs. Furthermore, the lists of signaled AED ADRs are analyzed to answer questions related to the common ADRs of AEDs and the similarities between AEDs in terms of their signaled ADRs. The consistency of the drawn answers with the existing pharmaceutical knowledge suggests the utility of the data from online health communities for AED-related knowledge discovery tasks.
Highlights
With an estimated 65 million people having epilepsy worldwide [1] and an annual rate ranging from 30 to 50 per 100,000 individuals [2], epilepsy is considered the most common serious neurological disorder after stroke
Association rule mining approaches are aimed at mining the association rule of the form drug ⇒ adverse drug reactions (ADRs): Common measures used in association rule mining are support, confidence, and lift [14]
The simple operation does not make statistical soundness in many cases because it does not adjust for the popularity of individual drug or correlation [57]
Summary
With an estimated 65 million people having epilepsy worldwide [1] and an annual rate ranging from 30 to 50 per 100,000 individuals [2], epilepsy is considered the most common serious neurological disorder after stroke. The current AEDs still fail to control seizures in 20–30% of all epilepsy patients [5, 6] Besides their use for epilepsy treatment, AEDs are extensively used to treat other conditions, including migraine, neuropathic pain, bipolar disorder, anxiety, and many other disorders [7]. With this wide prevalence and a reported yearly growth of AED usage, of new ones [7,8,9], their safety in use has become a major concern
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