Automated misspelling detection and correction in clinical free-text records
Automated misspelling detection and correction in clinical free-text records
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DOI：
10.1016/j.jbi.2015.04.008
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发表时间：
2015-06-01
影响因子：
4.5
通讯作者：
Zhou, Li
中科院分区：
文献类型：
--
作者：
Lai, Kenneth H.;Topaz, Maxim;Zhou, Li
准确的电子健康记录对于临床护理和研究以及确保患者安全非常重要。为了确保正确解释医疗记录，纠正拼写错误的单词至关重要。本文描述了一个医学文本拼写纠正系统的开发。我们的拼写检查器是基于香农的噪声信道模型，并使用广泛的字典编译从许多来源。我们还使用命名实体识别，以便名称不会被错误地纠正为拼写错误。我们将拼写检查器应用于三种不同类型的自由文本数据：临床笔记，过敏条目和药物订单：并评估其拼写错误检测和纠正的性能。我们的拼写检查器的检测性能高达94.4%，纠正准确率高达88.2%。我们表明，高性能的拼写校正是可能的各种临床文件。(C)2015 Elsevier Inc. All rights reserved.
Accurate electronic health records are important for clinical care and research as well as ensuring patient safety. It is crucial for misspelled words to be corrected in order to ensure that medical records are interpreted correctly. This paper describes the development of a spelling correction system for medical text. Our spell checker is based on Shannon's noisy channel model, and uses an extensive dictionary compiled from many sources. We also use named entity recognition, so that names are not wrongly corrected as misspellings. We apply our spell checker to three different types of free-text data: clinical notes, allergy entries, and medication orders: and evaluate its performance on both misspelling detection and correction. Our spell checker achieves detection performance of up to 94.4% and correction accuracy of up to 88.2%. We show that high-performance spelling correction is possible on a variety of clinical documents. (C) 2015 Elsevier Inc. All rights reserved.