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.

Objective: Obstructive Sleep Apnea Hypopnea Syndrome (OSAHS) can cause excessive daytime sleepiness and cognitive decline due to long-term nocturnal hypoxia. Without timely treatment, it may increase the risk of obesity, coronary heart disease, stroke, and other serious disorders. However, OSAHS is often underdiagnosed because the standard detection method, overnight polysomnography (PSG), is expensive and available only in limited medical facilities. This study aimed to develop a lower-cost and more accurate approach for detecting OSAHS using electrocardiogram (ECG) signals and biometric data. Method: We proposed a multimodal feature fusion framework that integrated ECG features extracted through a long short-term memory (LSTM) network with biometric features obtained via support vector machines (SVM). The fused features were classified through a fully connected layer to detect OSAHS. Two independent databases were used to evaluate the performance of the proposed method. Results: Experimental results showed that the LSTM–SVM fusion model achieved an accuracy of 97.1%, outperforming conventional classification models. In addition, it achieved 92% accuracy on a separate dataset, demonstrating strong generalization ability and potential for practical clinical application. Conclusion: By combining LSTM-extracted ECG features with SVM-based biometric features, the proposed multimodal fusion method provided highly effective OSAHS detection. The findings suggest considerable potential for the use of this approach in real medical environments.