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

Abstract

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.

Transformer-Based Classification of User Queries for Medical Consultancy

“…In the realm of medical text classification, the research paper titled "Automatic Medical Specialty Classification Based on Patients' Description of Their Symptoms" [4] presents a significant contribution to the field. The study proposes a pioneering Hybrid Model (HyM) that combines multiple deep learning techniques, including LSTM, TEXT-CNN, BERT, and TF-IDF, along with an attention mechanism to address the critical challenge of accurately directing patients to the appropriate medical specialty based on their symptom descriptions.…”