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Using Deep Learning for Automatic Icd-10 Classification from Free-Text Data

Ssu-Ming Wang, Yu-Hsuan Chang, Lu-Cheng Kuo, Feipei Lai, Yun-Nung Chen, Fei-Yun Yu, Chih-Wei Chen, Zong-Wei Li, Yufang Chung

. 2020 ; 16 (1) : 1-10.

Jazyk angličtina Země Česko

Typ dokumentu práce podpořená grantem

Perzistentní odkaz   https://www.medvik.cz/link/bmc20009456

Background: Classifying diseases into ICD codes has mainly relied on human reading a large amount of written materials, such as discharge diagnoses, chief complaints, medical history, and operation records as the basis for classification. Coding is both laborious and time consuming because a disease coder with professional abilities takes about 20 minutes per case in average. Therefore, an automatic code classification system can significantly reduce the human effort. Objectives: This paper aims at constructing a machine learning model for ICD-10 coding, where the model is to automatically determine the corresponding diagnosis codes solely based on free-text medical notes. Methods: In this paper, we apply Natural Language Processing (NLP) and Recurrent Neural Network (RNN) architecture to classify ICD-10 codes from natural language texts with supervised learning. Results: In the experiments on large hospital data, our predicting result can reach F1-score of 0.62 on ICD-10-CM code. Conclusion: The developed model can significantly reduce manpower in coding time compared with a professional coder.

Citace poskytuje Crossref.org

Bibliografie atd.

Literatura

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