Identification of Suicide-Related Events Through Network Analysis of Adverse Event Reports
In the treatment of depression, it is essential to monitor for early warnings of suicide.
The aim of this study was to identify the symptoms that would suggest a high suicide risk by analyzing data obtained from the US Food and Drug Administration Adverse Event Reporting System (FAERS) of selective serotonin reuptake inhibitors.
Using FAERS reports from 1997 to the second quarter of 2012, we constructed the co-occurrence network of adverse events. From this network, we extracted the events that were strongly connected to suicidal events (suicidal attempts, suicidal ideation, suicidal behavior, and complete suicide) by means of the community detection method.
We succeeded in obtaining a list of suicide-related adverse events. Owing to the randomness inherent in the algorithms of community detection, we found that the obtained list differed according to each trial of analysis. However, the lists we derived show considerable efficiency in identifying suicidal events.
The network analysis appears to be a promising method for identifying signals of suicide.
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