Purpose/Significance The paper discusses the application of artificial intelligence technology to the key entity recognition ofunstructured text data in the electronic medical records of lymphedema patients.Method/Process It expounds the solution of model fine-tuning training under the background of sample scarcity,a total of 594 patients admitted to the department of lymphatic surgery of Beijing Shijitan Hospital,Capital Medical University are selected as the research objects.The prediction layer of the GlobalPointer model is fine-tuned according to 15 key entity categories labeled by clinicians,nested and non-nested key entities are identified with its glob-al pointer.The accuracy of the experimental results and the feasibility of clinical application are analyzed.Result/Conclusion After fine-tuning,the average accuracy rate,recall rate and Macro_F1 ofthe model are 0.795,0.641 and 0.697,respectively,which lay a foundation for accurate mining of lymphedema EMR data.
关键词
淋巴水肿/电子病历/命名实体识别/自然语言处理/医学
Key words
lymphedema/electronic medical records/named entity recognition/natural language processing/medicine
维普
万方数据