Patient stratification and identification of adverse event correlations in the space of 1190 drug related adverse events
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Patient stratification and identification of adverse event correlations in the space of 1190 drug related adverse events. / Roitmann, Eva; Eriksson, Robert; Brunak, Søren.
In: Frontiers in Physiology, Vol. 5, 332, 11.09.2014.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Patient stratification and identification of adverse event correlations in the space of 1190 drug related adverse events
AU - Roitmann, Eva
AU - Eriksson, Robert
AU - Brunak, Søren
PY - 2014/9/11
Y1 - 2014/9/11
N2 - 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 (EMRs) 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 2347 patients extracted from EMRs from a mental health center 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.
AB - 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 (EMRs) 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 2347 patients extracted from EMRs from a mental health center 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.
U2 - 10.3389/fphys.2014.00332
DO - 10.3389/fphys.2014.00332
M3 - Journal article
C2 - 25249979
VL - 5
JO - Frontiers in Physiology
JF - Frontiers in Physiology
SN - 1664-042X
M1 - 332
ER -
ID: 127248236