Tsai Chung-Hung, Liu Kuan-Hung, Cheng Da-Chuan
Institute of Allied Health Sciences, College of Medicine, National Cheng Kung University, Tainan 701, Taiwan.
Department of Family Medicine, An Nan Hospital, China Medical University, Tainan 709, Taiwan.
Diagnostics (Basel). 2024 Feb 2;14(3):329. doi: 10.3390/diagnostics14030329.
This study aims explore the feasibility of using neural network (NNs) and deep learning to diagnose three common respiratory diseases with few symptom words. These three diseases are nasopharyngitis, upper respiratory infection, and bronchitis/bronchiolitis. Through natural language processing, the symptom word vectors are encoded by GPT-2 and classified by the last linear layer of the NN. The experimental results are promising, showing that this model achieves a high performance in predicting all three diseases. They revealed 90% accuracy, which suggests the implications of the developed model, highlighting its potential use in assisting patients' understanding of their conditions via a remote diagnosis. Unlike previous studies that have focused on extracting various categories of information from medical records, this study directly extracts sequential features from unstructured text data, reducing the effort required for data pre-processing.
本研究旨在探讨使用神经网络（NNs）和深度学习，仅通过少量症状词汇来诊断三种常见呼吸道疾病的可行性。这三种疾病分别是鼻咽炎、上呼吸道感染和支气管炎/细支气管炎。通过自然语言处理，症状词向量由GPT-2进行编码，并由神经网络的最后一个线性层进行分类。实验结果很有前景，表明该模型在预测这三种疾病方面均具有高性能。其准确率达到了90%，这显示了所开发模型的意义，突出了其在通过远程诊断协助患者了解自身病情方面的潜在用途。与以往专注于从医疗记录中提取各类信息的研究不同，本研究直接从非结构化文本数据中提取序列特征，减少了数据预处理所需的工作量。
