Department of Clinical Pharmacology, Austin Health, Level 5, Lance Townsend Building, Studley Rd, Heidelberg, VIC, 3084, Australia.
Department of Medicine, University of Melbourne, Parkville, VIC, Australia.
Drug Saf. 2019 Jun;42(6):721-725. doi: 10.1007/s40264-018-00794-y.
Adverse drug reaction (ADR) detection in hospitals is heavily reliant on spontaneous reporting by clinical staff, with studies in the literature pointing to high rates of underreporting [1]. International Classification of Diseases, 10th Revision (ICD-10) codes have been used in epidemiological studies of ADRs and offer the potential for automated ADR detection systems.
The aim of this study was to develop an automated ADR detection system based on ICD-10 codes, using machine-learning algorithms to improve accuracy and efficiency.
For a 12-month period from December 2016 to November 2017, every inpatient episode receiving an ICD-10 code in the range Y40.0-Y59.9 (ADR code) was flagged for review as a potential ADR. Each flagged admission was assessed by an expert pharmacist and, if needed, reviewed at regular ADR committee meetings. For each report, a determination was made about ADR probability and severity. The dataset was randomly split into training and test sets. A machine-learning model using the random forest algorithm was developed on the training set to discriminate between true and false ADR reports. The model was then applied to the test set to assess accuracy using the area under the receiver operating characteristic (AUC).
In the study period, 2917 Y40.0-Y59.9 codes were applied to admissions, resulting in 245 ADR reports after review. These 245 reports accounted for 44.5% of all ADR reporting in our hospital in the study period. A random forest model built on the training set was able to discriminate between true and false reports on the test set with an AUC of 0.803.
Automated ADR detection using ICD-10 coding significantly improved ADR detection in the study period, with improved discrimination between true and false reports by applying a machine-learning model.
医院内药物不良反应（ADR）的检测主要依赖于临床医护人员的自发报告，文献中的研究表明报告率普遍较低[1]。国际疾病分类第 10 版（ICD-10）代码已被用于 ADR 的流行病学研究，并为自动 ADR 检测系统提供了潜力。
本研究旨在开发一种基于 ICD-10 代码的自动 ADR 检测系统，使用机器学习算法提高准确性和效率。
在 2016 年 12 月至 2017 年 11 月的 12 个月期间，对每个应用 ICD-10 代码 Y40.0-Y59.9（ADR 代码）范围的住院患者进行标记，以便对其进行潜在 ADR 的审查。对每个标记的入院病例由一名专家药剂师进行评估，如果需要，在常规 ADR 委员会会议上进行审查。对于每个报告，确定 ADR 的可能性和严重程度。数据集被随机分为训练集和测试集。在训练集上使用随机森林算法开发了一个机器学习模型，以区分真实和虚假的 ADR 报告。然后将模型应用于测试集，使用接收者操作特征（ROC）曲线下的面积（AUC）来评估准确性。
在研究期间，共应用了 2917 个 Y40.0-Y59.9 代码，审查后得出 245 份 ADR 报告。这些报告占研究期间我院所有 ADR 报告的 44.5%。在训练集上构建的随机森林模型能够在测试集上区分真实和虚假报告，AUC 为 0.803。
使用 ICD-10 编码进行自动 ADR 检测在研究期间显著提高了 ADR 的检测率，通过应用机器学习模型提高了真实和虚假报告之间的区分度。
