A Clustering Framework for Patient Phenotyping with Application to Adverse Drug Events

被引:0
作者
Bampa, Maria [1 ]
Papapetrou, Panagiotis [1 ]
Hollmen, Jaakko [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
adverse drug events; clustering aggregation; clustering; electronic health records;
D O I
10.1109/CBMS49503.2020.00041
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
We present a clustering framework for identifying patient groups with Adverse Drug Reactions from Electronic Health Records (EHRs). The increased adoption of EHRs has brought changes in the way drug safety surveillance is carried out and plays an important role in effective drug regulation. Unsupervised machine learning methods using EHRs as their input can identify patients that share common meaningful information, without the need for expert input. In this work, we propose a generalized framework that exploits the strengths of different clustering algorithms and via clustering aggregation identifies consensus patient cluster profiles. Moreover, the inherent hierarchical structure of diagnoses and medication codes is exploited. We assess the statistical significance of the produced clusterings by applying a randomization technique that keeps the data distribution margins fixed, as we are interested in evaluating information that is not conveyed by the marginal distributions. The experimental findings suggest that the framework produces medically meaningful patient groups with regard to adverse drug events by investigating two use-cases, i.e., aplastic anaemia and drug-induced skin eruption.
引用
收藏
页码:177 / 182
页数:6
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