Temporal Graph Neural Network based Approach to Identify Safety Signals for Pharmacovigilance Analytics

Author(s): Vishal Pandey, Pravar Kulbhushan, Sakshi Tyagi, Sudipta Mukhopadhyay, Jacob Vishal, Rik Das, Indrajit Kar

Pharmacovigilance has gained much prominence to identify the adverse signal originating from the use of a drug and subsequent reviews. Pharmacovigilance analytics can be applied to gain insights by integrating data related to medical products from multiple sources and applying techniques to search, compare and summarize them. Identifying the drug adversities prior with the help of pharmacovigilance analytics can help to reduce the risk of unfavourable outcomes in selecting the line of treatment. However, multiple sources (medical reports, review websites, online platforms, etc.) exist for adverse signal detection, which makes it challenging to extract the information of interest manually.

The gap is readily identified by the authors and is addressed in this work by proposing a temporal graph neural network-based approach. The existing Named Entity Recognition (NER) techniques are not efficient enough to identify and extract the causal relationship between the drug and the events based on but not limited to, the onset of the event, giving insights like the type of ADR. In contrast, we propose a temporal graph neural network-based approach which includes both entity recognition as well as event extraction, followed by finding the causal relationship between the drug and the events. The technique uses temporal topological information, word dependency parsing, edge information, which represents the relationship among the entities, and Node Embeddings which are vectors which reflect properties of nodes in a network. The proposed methodology has exhibited improved results in comparison to the state of the art and has shown statistical significance in performance measures.

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