实时临床缩写词消歧的初步研究
A Preliminary Study of Clinical Abbreviation Disambiguation in Real Time.
作者信息
Wu Y, Denny J C, Rosenbloom S T, Miller R A, Giuse D A, Song M, Xu H
机构信息
School of Biomedical Informatics, The University of Texas Health Science Center at Houston , Houston, Texas, USA.
Department of Biomedical Informatics Camridge, Vanderbilt University , Nashville, Tennessee, USA.
出版信息
Appl Clin Inform. 2015 Jun 3;6(2):364-74. doi: 10.4338/ACI-2014-10-RA-0088. eCollection 2015.
OBJECTIVE
To save time, healthcare providers frequently use abbreviations while authoring clinical documents. Nevertheless, abbreviations that authors deem unambiguous often confuse other readers, including clinicians, patients, and natural language processing (NLP) systems. Most current clinical NLP systems "post-process" notes long after clinicians enter them into electronic health record systems (EHRs). Such post-processing cannot guarantee 100% accuracy in abbreviation identification and disambiguation, since multiple alternative interpretations exist.
METHODS
Authors describe a prototype system for real-time Clinical Abbreviation Recognition and Disambiguation (rCARD) - i.e., a system that interacts with authors during note generation to verify correct abbreviation senses. The rCARD system design anticipates future integration with web-based clinical documentation systems to improve quality of healthcare records. When clinicians enter documents, rCARD will automatically recognize each abbreviation. For abbreviations with multiple possible senses, rCARD will show a ranked list of possible meanings with the best predicted sense at the top. The prototype application embodies three word sense disambiguation (WSD) methods to predict the correct senses of abbreviations. We then conducted three experments to evaluate rCARD, including 1) a performance evaluation of different WSD methods; 2) a time evaluation of real-time WSD methods; and 3) a user study of typing clinical sentences with abbreviations using rCARD.
RESULTS
Using 4,721 sentences containing 25 commonly observed, highly ambiguous clinical abbreviations, our evaluation showed that the best profile-based method implemented in rCARD achieved a reasonable WSD accuracy of 88.8% (comparable to SVM - 89.5%) and the cost of time for the different WSD methods are also acceptable (ranging from 0.630 to 1.649 milliseconds within the same network). The preliminary user study also showed that the extra time costs by rCARD were about 5% of total document entry time and users did not feel a significant delay when using rCARD for clinical document entry.
CONCLUSION
The study indicates that it is feasible to integrate a real-time, NLP-enabled abbreviation recognition and disambiguation module with clinical documentation systems.
目的
为节省时间，医疗保健提供者在撰写临床文档时经常使用缩写。然而，作者认为明确无误的缩写常常会使其他读者感到困惑，包括临床医生、患者和自然语言处理（NLP）系统。目前大多数临床NLP系统在临床医生将记录录入电子健康记录系统（EHR）很久之后才进行“后处理”。由于存在多种不同的解释，这种后处理无法保证缩写识别和消除歧义的准确率达到100%。
方法
作者描述了一种用于实时临床缩写识别与消除歧义（rCARD）的原型系统，即一种在生成记录时与作者交互以验证正确缩写含义的系统。rCARD系统设计预期未来将与基于网络的临床文档系统集成，以提高医疗记录的质量。当临床医生录入文档时，rCARD将自动识别每个缩写。对于有多种可能含义的缩写，rCARD将显示一个可能含义的排序列表，最佳预测含义排在首位。该原型应用体现了三种词义消歧（WSD）方法来预测缩写的正确含义。然后我们进行了三项实验来评估rCARD，包括：1）不同WSD方法的性能评估；2）实时WSD方法的时间评估；3）使用rCARD输入含缩写临床句子的用户研究。
结果
使用包含25个常见、高度模糊临床缩写的4721个句子，我们的评估表明，rCARD中实现的基于最佳配置文件的方法实现了合理的WSD准确率，为88.8%（与支持向量机（SVM）的89.5%相当），并且不同WSD方法的时间成本也是可以接受的（在同一网络内从0.630毫秒到1.649毫秒不等）。初步用户研究还表明，rCARD带来的额外时间成本约为文档录入总时间的5%，并且用户在使用rCARD进行临床文档录入时并未感到明显延迟。
结论
该研究表明，将实时、启用NLP的缩写识别和消除歧义模块与临床文档系统集成是可行的。
Zotero 插件