@文章{信息:doi/10.2196/37213，作者=“李士成、邓士成、李宗、张、徐、陈、陆明、杨、涛、奇、蒋一帆、太娇”，标题=“基于序列Motif发现工具识别表型叙述语言模式的中国电子健康档案深度表型:算法开发与验证”，期刊=“J医学互联网研究”，年=“2022”，月=“6”，日=“3”，卷=“24”，数=“6”，页数=“e37213”，关键词=“深度表型;中国电子医疗纪录;语言模式;主题发现;背景:电子病历中的表型信息主要以非结构化的自由文本形式记录，不能直接用于临床研究。基于电子病历的深度表型分型方法能够以较高的保真度构建电子病历中的表型信息，成为医学信息学研究的热点。然而，开发一种针对非英语电子病历(即中文电子病历)的深度表型分型方法具有挑战性。虽然中国存在大量的EHR资源，但适合开发深度表型方法的细粒度注释数据有限。在如此低资源的情况下，开发中国电子病历的深度表型分型方法具有挑战性。目的:本研究旨在基于有限的细粒度标注数据，开发一种具有良好泛化能力的中文电子病历深度表型分型方法。 Methods: The core of the methodology was to identify linguistic patterns of phenotype descriptions in Chinese EHRs with a sequence motif discovery tool and perform deep phenotyping of Chinese EHRs by recognizing linguistic patterns in free text. Specifically, 1000 Chinese EHRs were manually annotated based on a fine-grained information model, PhenoSSU (Semantic Structured Unit of Phenotypes). The annotation data set was randomly divided into a training set (n=700, 70{\%}) and a testing set (n=300, 30{\%}). The process for mining linguistic patterns was divided into three steps. First, free text in the training set was encoded as single-letter sequences (P: phenotype, A: attribute). Second, a biological sequence analysis tool---MEME (Multiple Expectation Maximums for Motif Elicitation)---was used to identify motifs in the single-letter sequences. Finally, the identified motifs were reduced to a series of regular expressions representing linguistic patterns of PhenoSSU instances in Chinese EHRs. Based on the discovered linguistic patterns, we developed a deep-phenotyping method for Chinese EHRs, including a deep learning--based method for named entity recognition and a pattern recognition--based method for attribute prediction. Results: In total, 51 sequence motifs with statistical significance were mined from 700 Chinese EHRs in the training set and were combined into six regular expressions. It was found that these six regular expressions could be learned from a mean of 134 (SD 9.7) annotated EHRs in the training set. The deep-phenotyping algorithm for Chinese EHRs could recognize PhenoSSU instances with an overall accuracy of 0.844 on the test set. For the subtask of entity recognition, the algorithm achieved an F1 score of 0.898 with the Bidirectional Encoder Representations from Transformers--bidirectional long short-term memory and conditional random field model; for the subtask of attribute prediction, the algorithm achieved a weighted accuracy of 0.940 with the linguistic pattern--based method. Conclusions: We developed a simple but effective strategy to perform deep phenotyping of Chinese EHRs with limited fine-grained annotation data. Our work will promote the second use of Chinese EHRs and give inspiration to other non--English-speaking countries. ", issn="1438-8871", doi="10.2196/37213", url="//www.mybigtv.com/2022/6/e37213", url="https://doi.org/10.2196/37213", url="http://www.ncbi.nlm.nih.gov/pubmed/35657661" }