Automated encoding of clinical documents based on natural language processing
Automated encoding of clinical documents based on natural language processing
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DOI：
10.1197/jamia.m1552
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发表时间：
2004-09-01
影响因子：
6.4
通讯作者：
Hripcsak, G
中科院分区：
文献类型：
--
作者：
Friedman, C;Shagina, L;Hripcsak, G
目的：本研究的目的是开发一种基于自然语言处理（NLP）的方法，自动映射到整个临床文件的代码与修饰符，并定量评估的method.Methods：现有的NLP系统，MedLEE，适应自动生成代码。该方法涉及匹配MecILEE生成的结构化输出（由发现和修饰符组成），以获得最具体的代码。在两项独立的研究中评估了统一医学语言系统（UMLS）编码的召回率和精确度。回忆使用150个随机选择的句子的测试集进行测量，这些句子使用MedLEE处理。将结果与由七位专家手动确定的参考标准进行比较。精确度进行了测量，使用第二个测试集的150个随机选择的句子，UMLS代码自动生成的方法，然后由experts.Results验证：召回的系统UMLS编码的所有条款。77（95% CI .72-81），而对于具有相应UMLS代码的编码术语，召回率为.83（.79-.87）。提取所有项的系统的召回率为0.84（0.81 -88）。专家的召回范围从0.69到0.91提取术语。系统的精确度为0.89（0.87 -91），专家的精确度为0.61 - 0.91。结论：基于自然语言处理的方法实现了临床相关信息的提取和UMLS编码。这一招，看起来，比之六位高手，都要略胜一筹。该方法的优点是它将文本与其他相关信息沿着映射到代码，使编码输出适合有效检索。
Objective: The aim of this study was to develop a method based on natural language processing (NLP) that automatically maps an entire clinical document to codes with modifiers and to quantitatively evaluate the method.Methods: An existing NLP system, MedLEE, was adapted to automatically generate codes. The method involves matching of structured output generated by MecILEE consisting of findings and modifiers to obtain the most specific code. Recall and precision applied to Unified Medical Language System (UMLS) coding were evaluated in two separate studies. Recall was measured using a test set of 150 randomly selected sentences, which were processed using MedLEE. Results were compared with a reference standard determined manually by seven experts. Precision was measured using a second test set of 150 randomly selected sentences from which UMLS codes were automatically generated by the method and then validated by experts.Results: Recall of the system for UMLS coding of all terms was. 77 (95% CI .72-81), and for coding terms that had corresponding UMLS codes recall was .83 (.79-.87). Recall of the system for extracting all terms was .84 (.81-88). Recall of the experts ranged from .69 to .91 for extracting terms. The precision of the system was .89 (.87-91), and precision of the experts ranged from .61 to .91.Conclusion: Extraction of relevant clinical information and UMLS coding were accomplished using a method based on NLP. The method appeared to be comparable to or better than six experts. The advantage of the method is that it maps text to codes along with other related information, rendering the coded output suitable for effective retrieval.