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GA-ADE: a novel approach based on graph algorithm to improves the detection of adverse drug events

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Abstract

The adverse drug event (ADE) is an unexpected and harmful consequence of drug usege. Identifying the association between the use of drugs and adverse events from biomedical literature can contribute a lot to drug safety supervision. Such identification can not only assist drug safety monitoring, but also correct known dependencies among events. In this paper,we propose a novel approach based on graph algorithm to detect adverse drug events(GA-ADE). In our approach, we first construct a graph using candidate ADE extracted from biomedical literature. We then propose a method to select important vertices from the graph as core vertices, and design a Personal Rank algorithm using the core vertices for clustering to build subgraphs. Lastly, the correlation between the drug and the event is calculated based on the subgraphs. Experiments show that our approach is feasible.

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Acknowledgments

This research was partially supported by the Scientfic Research Foundation of Graduate School of South China Normal University, the Natural Science Foundation of Guangdong Province, China(2015A030310509), the Public Research and Capacity Building in Guangdong Province, China(2016A030303055), the Major Science and Technology projects of Guangdong Province, China(2016B030305004, 2016B010109008, 2016B010124008) and the National Natural Science Foundation of China(61272067).

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Authors and Affiliations

  1. School of Computer Science, South China Normal University, Guangzhou, China

    Xingcheng Wu, Jia Zhu, Danyang Xiao, Xueqin Lin & Rui Ding

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  1. Xingcheng Wu
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  2. Jia Zhu
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Correspondence to Jia Zhu.

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Wu, X., Zhu, J., Xiao, D. et al. GA-ADE: a novel approach based on graph algorithm to improves the detection of adverse drug events. Multimed Tools Appl 77, 3493–3507 (2018). https://doi.org/10.1007/s11042-017-5162-3

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