Automatic medical specialty classification based on patients' description of their symptoms

• Published in: BMC medical informatics and decision making Vol. 23; no. 1; p. 15
• Main Authors: Mao, Chao, Zhu, Quanjing, Chen, Rong, Su, Weifeng
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 20.01.2023; BioMed Central; BMC
• Subjects: Analysis; Artificial intelligence; Attention; BERT; Child; China; Classification; Convolutional neural network; Datasets; Decision making; Deep learning; Efficiency; Evaluation; Health informatics; Hospitals; Humans; Machine learning; Machine translation; Medical personnel; Medical specialty classification; Medical tests; Medicine; Neural networks; Neural Networks, Computer; Otolaryngology; Patients; Pediatrics; Physicians; Recommender systems; Registration; Text categorization; China; BERT; Medical specialty classification; Convolutional neural network; Registration; Attention
• ISSN: 1472-6947; 1472-6947
• DOI: 10.1186/s12911-023-02105-7

In China, patients usually determine their medical specialty before they register the corresponding specialists in the hospitals. This process usually requires a lot of medical knowledge for the patients. As a result, many patients do not register the correct specialty for the first time if they do not receive help from the hospitals. In this study, we try to automatically direct the patients to the appropriate specialty based on the symptoms they described. As far as we know, this is the first study to solve the problem. We propose a neural network-based model based on a hybrid model integrated with an attention mechanism. To prove the actual effect of this hybrid model, we utilized a data set of more than 40,000 items, including eight departments, such as Otorhinolaryngology, Pediatrics, and other common departments. The experiment results show that the hybrid model achieves more than 93.5% accuracy and has a high generalization capacity, which is superior to traditional classification models.