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.

• MeSH
• automatizované zpracování dat metody   MeSH
• deep learning * MeSH
• elektronické zdravotní záznamy MeSH
• mezinárodní klasifikace nemocí * MeSH
• neuronové sítě MeSH
• strojové učení MeSH
• ukládání a vyhledávání informací metody   statistika a číselné údaje   MeSH
• vizualizace dat MeSH
• zpracování přirozeného jazyka MeSH
• Publikační typ
• práce podpořená grantem MeSH