Mining disproportional frequent arrangements of event intervals for investigating adverse drug events

被引:2
|
作者
Lee, Zed [1 ]
Rebane, Jonathan [1 ]
Papapetrou, Panagiotis [1 ]
机构
[1] Stockholm Univ, Dept Comp & Syst Sci, Stockholm, Sweden
来源
2020 IEEE 33RD INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS(CBMS 2020) | 2020年
基金
瑞典研究理事会;
关键词
machine learning; temporal intervals; adverse drug events; PATIENT;
D O I
10.1109/CBMS49503.2020.00061
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Adverse drug events are pervasive and costly medical conditions, in which novel research approaches are needed to investigate the nature of such events further and ultimately achieve early detection and prevention. In this paper, we seek to characterize patients who experience an adverse drug event, represented as a case group, by contrasting them to similar control group patients who do not experience such an event. To achieve this goal, we utilize an extensive electronic patient record database and apply a combination of frequent arrangement mining and disproportionality analysis. Our results have identified how several adverse drug events are characterized in regards to frequent disproportional arrangements, where we highlight how such arrangements can provide additional temporal-based information compared to similar approaches.
引用
收藏
页码:289 / 292
页数:4
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