Automated classification of ICD-O-3 morphology code from pathology reports using text-mining and support vector machine / 预防医学

Objective@#To evaluate the accuracy of automated classification of ICD-O-3 morphology code from pathology reports by text-mining and support vector machine ( SVM ) , in order to provide basis for automated tumor coding in Chinese. @*Methods@#The tumor report cards of Zhejiang residents from 2017 to 2019 were collected from Chronic Disease Surveillance Information Management System of Zhejiang Province. According to ICD-O-3, the keywords of the pathology reports were extracted, and SVM was used for automatic classification. The classification results were compared with those of 16 professionals with more than two years of experience in tumor coding, and the accuracy rate, recall rate and F-score were calculated for effect evaluation. @*Results@#Totally 83 082 cases from 2017 to 2019 were included and were categorized into 17 morphological classifications, with 52 877 ( 63.65% ) cases of adenocarcinoma, squamous carcinoma and transitional cell carcinoma. A total of 1 090 keywords were enrolled into main corpus. The total F-score, accuracy rate and recall rate are 85.69, 77.20% and 96.27%, respectively. @*Conclusion@#Text-mining combined with SVM can improve the efficiency of ICD-O-3 morphology coding; however, the accuracy needs to be further improved.

Association between chronic hepatitis B virus infection anddiabetes among adults / 预防医学

Objective@#To explore the association between chronic hepatitis B virus infection and diabetes among adults.@*Methods@#The baseline data of China Kadoorie Biobank ( CKB ) study from Tongxiang of Zhejiang Province was used for analysis. Community residents were investigated in the study from August 2004 to May 2008, including questionnaire survey, physical measurement and biological sample test. Univariate and multivariate logistic regression models were used to estimate the association of chronic hepatitis B virus infection with diabetes.@*Results@#Totally 52 888 participants were included in the final analysis. The overall prevalence of HBsAg-positive was 3.55% ( N=1 877 ). The overall prevalence of diabetes was 5.17% ( N=2 733 ). The prevalence of HBsAg-positive in diabetic patients was 3.51% ( N=96 ). Both univariate and multivariate logistic regression models indicated that there was no association between chronic hepatitis B virus infection and diabetes（ P>0.05 ）. @*Conclusion@#No significant association has been found between chronic hepatitis B virus infection and diabetes among adults.