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Categorization of free-text drug orders using character-level recurrent neural networks.
International Journal of Medical Informatics ( IF 4.9 ) Pub Date : 2019-08-25 , DOI: 10.1016/j.ijmedinf.2019.05.020
Yarden Raiskin 1 , Carsten Eickhoff 2 , Patrick E Beeler 3
Affiliation  

BACKGROUND AND PURPOSE Manual annotation and categorization of non-standardized text ("free-text") of drug orders entered into electronic health records is a labor-intensive task. However, standardization is required for drug order analyses and has implications for clinical decision support. Machine learning could help to speed up manual labelling efforts. The objective of this study was to analyze the performance of deep machine learning methods to annotate non-standardized text of drug order entries with their therapeutically active ingredients. MATERIALS AND METHODS The data consisted of drug orders entered 8/2009-4/2014 into the electronic health records of inpatients at a large tertiary care academic medical center. We manually annotated the most frequent order entry patterns with the active ingredient they contain (e.g. "Prograf"⟵"Tacrolimus"). We heuristically included additional orders by means of character sequence comparisons to augment the training dataset. Finally, we trained and employed character-level recurrent deep neural networks to classify non-standardized text of drug order entries according to their active ingredients. RESULTS A total of 26,611 distinct order patterns were considered in our study, of which the top 7.6% (2028) had been annotated with one of 558 distinct ingredients, leaving 24,583 unlabeled observations. Character-level recurrent deep neural networks achieved a Mean Reciprocal Rank (MRR) of 98% and outperformed the best representative baseline, a trigram-based Support Vector Machine, by 2 percentage points. CONCLUSION Character-level recurrent deep neural networks can be used to map the active ingredient to non-standardized text of drug order entries, outperforming other representative techniques. While machine learning might help to facilitate categorization tasks, still a considerable amount of manual labelling and reviewing work is required to train such systems.

中文翻译:

使用字符级循环神经网络对自由文本药品订单进行分类。

背景和目的人工​​注释和分类输入电子健康记录的药品订单的非标准化文本(“自由文本”)是一项劳动密集型任务。但是,药物顺序分析需要标准化,这对临床决策支持具有影响。机器学习可以帮助加快手动贴标签的工作。这项研究的目的是分析深度机器学习方法的性能,以其治疗活性成分注释药品订单条目的非标准化文本。材料和方法数据包括在大型三级医疗学术医疗中心的患者电子健康记录中输入的8 / 2009-4 / 2014的药物订单。我们用它们所含的有效成分手动注释了最常见的订单输入模式(例如“ 结论字符级递归深层神经网络可用于将有效成分映射到药品订单条目的非标准化文本,胜过其他代表性技术。虽然机器学习可能有助于简化分类任务,但仍需要大量的手动标记和审查工作来训练此类系统。
更新日期:2019-11-01
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