Abstract:Objective To extract staging information of gastric cancer from unstructured surgical records automatically with natural language processing and evaluate the performance. Methods From 2016 to 2018, a total of 632 gastric cancer patients who
underwent surgery were collected from the electronic medical record system, and their surgical records were analyzed to determined the entities and attributes according to clinical problems. Two experienced clinicians annotated entities and attributes which were served as gold standard. On a scale of 3:1, 632 cases were randomly divided into training group and validation group. The extraction of recorded information mainly consists of two steps: firstly, the identification of medical entities; secondly, the extraction of
attributes. Precision, recall rate and F-measure were used to evaluate the performance. Results A total of 21319 entities and 4390 attributes were analyzed. The average precision, recall and F-measure of clinical entities were 0.84, 0.87 and 0.85 under strict
matching criteria. The average precision, recall and F-measure of attributes were 0.86, 0.88 and 0.87 under strict matching criteria. F-measures under relaxed matching criterion were all greater than that under strict matching. In validation group, 19.62% patient
have serosal invasion, 37.34% patient have enlarged lymph nodes and 4.43% patient have peritoneal metastasis. Conclusion This study presents a new hybrid method to extract gastric cancer staging information and will be likely to be applied in electronic medical
records of different systems in the future.
黄文鹏a,李莉明a,程铭b,李爱云a,梁盼a,雍刘亮a,高剑波a. 智能提取胃癌分期相关信息研究[J]. 中国医疗设备, 2021, 36(1): 40-43.
HUANG Wenpenga,LI Liminga,CHENG Mingb,LI Aiyuna,LIANG Pana,YONG Liulianga,GAO Jianboa. Development and Application of Image Segmentation in Tumor Radiotherapy. China Medical Devices, 2021, 36(1): 40-43.