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药物不良事件的检测:一种数据模型的提议

Detection of adverse drug events: proposal of a data model.

作者信息

Chazard Emmanuel, Merlin Béatrice, Ficheur Grégoire, Sarfati Jean-Charles, Beuscart Régis

机构信息

Lille university hospital, EA2694, Lille, France.

出版信息

Stud Health Technol Inform. 2009;148:63-74.

Abstract

Our main objective is to detect adverse drug events (ADEs) in former hospital stays. As ADEs are rare, that supposes to screen thousands of electronic health records (EHRs). For that purpose, we need to define a data model that has two main objectives: (1) being able to describe hospital stays from various hospitals (2) being tuned so as to prepare the data mining process: as ADEs are not flagged in the datasets, the data model must be optimized for ADE detection. The article presents the phases of the design and the data model that results from this work. It is compatible with many hospitals. It deals with diagnoses, drug prescriptions, lab results and administrative information. It allows for data mining and ADE detection in EHRs.

摘要

我们的主要目标是检测既往住院期间的药物不良事件(ADEs)。由于ADEs较为罕见,这意味着要筛查数千份电子健康记录(EHRs)。为此,我们需要定义一个具有两个主要目标的数据模型:(1)能够描述来自不同医院的住院情况;(2)进行调整以便为数据挖掘过程做好准备:由于数据集中未标记ADEs,数据模型必须针对ADE检测进行优化。本文介绍了这项工作的设计阶段以及由此产生的数据模型。它与许多医院兼容。它涉及诊断、药物处方、实验室结果和管理信息。它允许在EHRs中进行数据挖掘和ADE检测。

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