Chazard Emmanuel, Ficheur Grégoire, Bernonville Stéphanie, Luyckx Michel, Beuscart Régis
Université Lille Nord de France, Lille, France.
IEEE Trans Inf Technol Biomed. 2011 Nov;15(6):823-30. doi: 10.1109/TITB.2011.2165727. Epub 2011 Aug 22.
Adverse drug events (ADEs) are a public health issue. Their detection usually relies on voluntary reporting or medical chart reviews. The objective of this paper is to automatically detect cases of ADEs by data mining. 115,447 complete past hospital stays are extracted from six French, Danish, and Bulgarian hospitals using a common data model including diagnoses, drug administrations, laboratory results, and free-text records. Different kinds of outcomes are traced, and supervised rule induction methods (decision trees and association rules) are used to discover ADE detection rules, with respect to time constraints. The rules are then filtered, validated, and reorganized by a committee of experts. The rules are described in a rule repository, and several statistics are automatically computed in every medical department, such as the confidence, relative risk, and median delay of outcome appearance. 236 validated ADE-detection rules are discovered; they enable to detect 27 different kinds of outcomes. The rules use a various number of conditions related to laboratory results, diseases, drug administration, and demographics. Some rules involve innovative conditions, such as drug discontinuations.
药物不良事件（ADEs）是一个公共卫生问题。其检测通常依赖于自愿报告或病历审查。本文的目的是通过数据挖掘自动检测ADEs病例。使用包括诊断、药物给药、实验室结果和自由文本记录的通用数据模型，从六家法国、丹麦和保加利亚医院提取了115447份完整的既往住院病例。追踪不同类型的结果，并使用监督规则归纳方法（决策树和关联规则）来发现关于时间限制的ADE检测规则。然后由专家委员会对这些规则进行筛选、验证和重新组织。这些规则在规则库中进行描述，并在每个医疗部门自动计算一些统计数据，如结果出现的置信度、相对风险和中位延迟。发现了236条经过验证的ADE检测规则；它们能够检测27种不同类型的结果。这些规则使用了与实验室结果、疾病、药物给药和人口统计学相关的各种条件。一些规则涉及创新条件，如药物停用。
