Patient stratification and identification of adverse event correlations in the space of 1190 drug related adverse events.
Patient stratification and identification of adverse event correlations in the space of 1190 drug related adverse events.
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患者分层和鉴定1190个与药物相关的不良事件空间中不良事件相关性。
DOI：
10.3389/fphys.2014.00332
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发表时间：
2014
影响因子：
4
通讯作者：
Brunak S
中科院分区：
文献类型：
--
作者：
Roitmann E;Eriksson R;Brunak S
目的：由于药物引起的疾病，需要新的药物警戒方法。我们利用从电子病历（EMR）中通过文本挖掘提取的细粒度药物相关不良事件信息，根据患者的不良事件对患者进行分层，并确定不良事件的共同发生。方法：我们分析了从丹麦一家精神卫生中心的电子病历中提取的2347例患者不良事件特征的相似性。根据患者的不良事件特征对患者进行聚类，并将相似性表示为网络。评价了每个主要患者群中的不良事件集。还确定并列出了患者中不良事件的同时发生率（p值< 0.01）。结果：我们发现，每一组患者通常有一个最明显的不良事件。通过检查患者中不良事件的同时发生情况，识别出了可能值得关注的不良事件相关性，这些相关性可能需要进一步研究，并提供了进一步的患者分层机会。结论：我们已经证明了一种新的药物警戒方法的可行性，根据细粒度的不良事件特征对患者进行分层，这也使得识别不良事件相关性成为可能。在较大的数据集上使用，这种数据驱动的方法有可能揭示有关不良事件发生的未知模式。
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.
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影响因子：
2.6
作者：
Coloma, Preciosa M.;Trifiro, Gianluca;Sturkenboom, Miriam
通讯作者：
Sturkenboom, Miriam
影响因子：
4.2
作者：
Brown, EG;Wood, L;Wood, S
通讯作者：
Wood, S
影响因子：
2.9
作者：
Kierkegaard, Patrick
通讯作者：
Kierkegaard, Patrick
影响因子：
2.6
作者：
Coloma, Preciosa M.;Schuemie, Martijn J.;Sturkenboom, Miriam
通讯作者：
Sturkenboom, Miriam
DOI：
10.1111/j.2517-6161.1995.tb02031.x
发表时间：
1995-01-01
影响因子：
5.8
作者：
BENJAMINI, Y;HOCHBERG, Y
通讯作者：
HOCHBERG, Y