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| Automated classification of ICD-O-3 morphology code from pathology reports using text-mining and support vector machine |
| PAN Jin, GONG Weiwei, FEI Fangrong, WANG Meng, ZHOU Xiaoyan, HU Ruying, ZHONG Jieming
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| Department of Non-communicable Disease Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang 310051, China |
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Abstract 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.
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Received: 09 June 2020
Revised: 21 December 2020
Published: 16 March 2021
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