ArticleBMC medical informatics and decision making2023
Automatic medical specialty classification based on patients' description of their symptoms.
Article in BMC medical informatics and decision making, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Early identification of risk factors for obstructive sleep apnea hypopnea syndrome based on large language models.Frontiers in medicine · 2026Article
- Multimodal ECG and biometric data fusion for improved detection of obstructive sleep apnea hypopnea syndrome.Frontiers in medicine · 2026Article
- A hybrid model integrating RoBERTa, TF-IDF, and attention mechanism for medical query intent classification.Scientific reports · 2025Article
- Identification and cause analysis on unplanned reoperations by text classification approach.Scientific reports · 2025Article
- Automated OSAHS detection from ECG using temporal convolutional network.Scientific reports · 2025Article
- Applications of Natural Language Processing in Otolaryngology: A Scoping Review.The Laryngoscope · 2025Article
- Identifying Kidney Stone Risk Factors Through Patient Experiences With a Large Language Model: Text Analysis and Empirical Study.Journal of medical Internet research · 2025Article
- Enhancing treatment decision-making for low back pain: a novel framework integrating large language models with retrieval-augmented generation technology.Frontiers in medicine · 2025Article
- Entity-enhanced BERT for medical specialty prediction based on clinical questionnaire data.PloS one · 2025Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.