摘要：

Abstract:

【Purpose/significance】 Named entity recognition (NER), as a core component of medical record processing, is crucial to im⁃

proving the accuracy and efficiency of electronic medical record processing. Especially in the field of processing Chinese medical re⁃

cords, NER tasks face more challenges due to the complexity of Chinese. Therefore, developing an effective named entity recognition

model for Chinese medical records is of great value for improving the information extraction and data processing process of medical re⁃

cords.【Method/process】 A novel framework NER-CMR (Chinese Medical Records Named Entity Recognition) is proposed, aiming to

overcome the limitations of existing NER methods in Chinese medical records. The NER-CMR framework solves the problems of en⁃

tity word nesting and boundary identification in traditional NER by combining contextual information such as popular continuous

words and phrases. Specifically, the framework extracts adjacencies, co-occurrences, and dependencies between characters from re⁃

lated words and phrases, and this information is subsequently fused into the NER neural model. NER-CMR includes character encod⁃

ing module, word embedding module, graph building module, fusion module and CRF module.【Result/conclusion】 Through compre⁃

hensive experiments on CCKS, a widely used Chinese medical record dataset, and DIABETES real diabetes Chinese dataset, NER

CMR demonstrated its ability to outperform the baseline model in recognition performance. In addition, as a Chinese NER task pro⁃

cessing framework that introduces graph neural networks, this model has the flexibility of module replacement, providing a new devel⁃

opment direction for the research field of named entity recognition in Chinese electronic medical records.

【

Innovation/limitation】 A

network graph based on graph attention mechanism is proposed, a fusion layer is designed to realize multi-graph fusion processing,

and two strategies are further used to deal with the noise problem caused by incorrect relationships, but there is a lack of case studies

on the application level of smart medical system.