JMIR Publications

ABSTRACT

Background:

Adverse reactions of drugs attract significant concerns in both clinical practice and public health monitoring. Multiple measures have been put into place to increase postmarketing surveillance of drug adverse effects and improve drug safety. These include implementing spontaneous reporting systems and developing automated natural language processing (NLP) systems from electronic health records and social media data to collect evidence of adverse drug events (ADEs) that can be further investigated as possible adverse reactions.

Objective:

While using social media for this purpose has potential, it is not clear whether it is a reliable source for this information. Our work aims to 1) develop NLP approaches to identify ADEs on social media and 2) assess the reliability of social media data to identify ADEs.

Methods:

We propose a collocated Long Short-Term Memory network model with attentive pooling and aggregated, contextual representation generated by a pretrained model. We applied the model on large-scale Twitter data to identify ADE-related tweets. We conducted a qualitative content analysis on those tweets to validate the reliability of social media data as a means to collect such information.

Results:

The model outperforms the variant without the contextual representations during both the validation and evaluation phases. Through the content analysis on ADE tweets, we observe that ADE-related discussions referred to seven ADE themes. Mental health-related, sleep-related, and pain-related ADE discussions were most frequent. We also contrast known adverse drug reactions to f those mentioned in tweets.

Conclusions:

We observe a distinct improvement in the model using contextual information. However, results reveal weak generalizability of the current systems on unseen data. Additional research is needed to fully utilize social media data, and improve the robustness and reliability of NLP systems. The content analysis, on the other hand, shows that Twitter covers a sufficiently wide range of ADEs as well as known adverse reactions for drugs mentioned in tweets. Our work demonstrates that social media can be a reliable data source for collecting ADE mentions.