Patient stratification and identification of adverse event correlations in the space of 1,190 drug related adverse reactions

Frontiers in Physiology

Robert eEriksson¹  Søren eBrunak¹  Eva eRoitmann¹ 

[1] Faculty of Health and Medical Sciences, University of Copenhagen;Technical University of Denmark;

关键词: Data Mining;    Network analysis;    adverse events;    Electronic Medical Records;    adverse drugs reactions;    patient stratification;   

DOI  :  10.3389/fphys.2014.00332

来源: DOAJ

【 摘 要 】

Purpose New pharmacovigilance methods are needed as a consequence of the morbidity caused by drugs. We exploit fine-grained drug related adverse event information extracted by text mining from electronic medical records to stratify patients based on their adverse events and to determine adverse event co-occurrences.Methods We analyzed the similarity of adverse event profiles of 2,347 patients extracted from electronic medical records from a mental health centre in Denmark. The patients were clustered based on their adverse event profiles and the similarities were presented as a network. The set of adverse events in each main patient cluster was evaluated. Co-occurrences of adverse events in patients (p-value<0.01) were identified and presented as well.Results We found that each cluster of patients typically had a most distinguishing adverse event. Examination of the co-occurrences of adverse events in patients led to the identification of potentially interesting adverse event correlations that may be further investigated as well as provide further patient stratification opportunities. Conclusions We have demonstrated the feasibility of a novel approach in pharmacovigilance to stratify patients based on fine-grained adverse event profiles, which also makes it possible to identify adverse event correlations. Used on larger data sets, this data-driven method has the potential to reveal unknown patterns concerning adverse event occurrences.

【 授权许可】

Unknown