PDF(Pubmed)
Abstract：
Large hospitals can be complex, with numerous discipline and subspecialty settings. Patients may have limited medical knowledge, making it difficult for them to determine which department to visit. As a result, visits to the wrong departments and unnecessary appointments are common. To address this issue, modern hospitals require a remote system capable of performing intelligent triage, enabling patients to perform self-service triage. To address the challenges outlined above, this study presents an intelligent triage system based on transfer learning, capable of processing multilabel neurological medical texts. The system predicts a diagnosis and corresponding department based on the patient\'s input. It utilizes the triage priority (TP) method to label diagnostic combinations found in medical records, converting a multilabel problem into a single-label one. The system considers disease severity and reduces the \"class overlapping\" of the dataset. The BERT model classifies the chief complaint text, predicting a primary diagnosis corresponding to the complaint. To address data imbalance, a composite loss function based on cost-sensitive learning is added to the BERT architecture. The study results indicate that the TP method achieves a classification accuracy of 87.47% on medical record text, outperforming other problem transformation methods. By incorporating the composite loss function, the system\'s accuracy rate improves to 88.38% surpassing other loss functions. Compared to traditional methods, this system does not introduce significant complexity, yet substantially improves triage accuracy, reduces patient input confusion, and enhances hospital triage capabilities, ultimately improving the patient\'s medical experience. The findings could provide a reference for intelligent triage development.
摘要：
大型医院可能很复杂，拥有众多的学科和亚专业设置。患者的医学知识可能有限，使他们难以确定访问哪个部门。因此,访问错误的部门和不必要的任命是常见的。为了解决这个问题，现代医院需要一个能够进行智能分诊的远程系统，使患者能够进行自助分诊。为了应对上述挑战，本研究提出了一种基于迁移学习的智能分诊系统，能够处理多标签神经医学文本。系统基于患者的输入来预测诊断和相应的科室。它利用分诊优先级（TP）方法来标记医疗记录中的诊断组合，将多标签问题转换为单标签问题。系统考虑疾病严重程度并减少数据集的“类重叠”。BERT模型对主要投诉文本进行了分类，预测与投诉相对应的初步诊断。为了解决数据不平衡，在BERT架构中添加了基于成本敏感学习的复合损失函数。研究结果表明,TP方法对病历文本的分类准确率达到87.47%,优于其他问题转换方法。通过合并复合损失函数，系统的准确率提高到88.38%，超过其他损失函数。与传统方法相比，这个系统不会引入显著的复杂性，但大大提高了分诊的准确性，减少患者输入混乱，增强医院分诊能力，最终改善患者的医疗体验。研究结果可为智能分诊开发提供参考。
