Abstract
Drug misuse may happen when patients do not follow the prescriptions and do actions which lead to potentially harmful situations, such as intakes of incorrect dosage (overuse or underuse) or drug use for indications different from those prescribed. Although such situations are dangerous, patients usually do not report the misuse of drugs to their physicians. Hence, other sources of information are necessary for studying these issues. We assume that online health fora can provide such information and propose to exploit them. The general purpose of our work is the automatic detection and classification of drug misuses by analysing user-generated data in French social media. To this end, we propose a multi-step method, the main steps of which are: (1) indexing of messages with extended vocabulary adapted to social media writing; (2) creation of typology of drug misuses; and (3) automatic classification of messages according to whether they contain drug misuses or not. We present the results obtained at different steps and discuss them. The proposed method permit to detect the misuses with up to 0.773 F-measure.
Highlights
According to the existing studies, between 3% Pouyanne et al (2000) and 20% Queneau et al (2007) of emergency admissions are caused by adverse drug reactions (ADRs)
We present and discuss the results according to the three main steps of the method: indexing of forum messages thanks to specific resources built for social media language; creation of typology of misuses; and automatic recognition and categorization of messages with drug misuses
We indicate the size of these lexica, and provide examples of items they offer in addition to the seeds, as well as their translations
Summary
According to the existing studies, between 3% Pouyanne et al (2000) and 20% Queneau et al (2007) of emergency admissions are caused by adverse drug reactions (ADRs). ADRs and DDIs have been studied by researchers (Bate et al, 1998; Bousquet et al, 2005; Duda et al, 2005; Trifirò et al, 2009; Aagaard et al, 2012; Segura-Bedmar et al, 2013; O’Connor et al, 2014; Ayvaz et al, 2015), but issues related to drug non-adherence have been poorly addressed up to now, especially with computational approaches In all these cases, the situation is harmful for the patients, who are exposed to potential safety risks. The situation is even worse than with the ADRs reporting, which does not exceed 5% (Moride et al, 1997; Lacoste-Roussillon et al, 2001) across the world
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