Abstract
Analyzing authors' sentiments in texts as a technique for identifying text polarity can be practical and useful in various fields, including medicine and dentistry. Currently, due to factors such as patients' limited knowledge about their condition, difficulties in accessing specialist doctors, or fear of illness, particularly in pandemic conditions, there might be a delay between receiving a radiology report and consulting a doctor. In some cases, this delay can pose significant risks to the patient, making timely decision-making crucial. Having an automatic system that can inform patients about the deterioration of their condition by analyzing the text of radiology reports could greatly impact timely decision-making. In this study, a dataset comprising 1,134 cone-beam computed tomography (CBCT) photo reports was collected from the Shiraz University of Medical Sciences. Each case was examined, and an expert labeled a severity level for the patient's condition on each document. After preprocessing all the text data, a deep learning model based on Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network architecture, known as CNN-LSTM, was developed to detect the severity level of the patient's problem based on sentiment analysis in the radiologist's report. The model's performance was evaluated on two datasets, each with two and four classes, in both imbalanced and balanced scenarios. Finally, to demonstrate the effectiveness of our model, we compared its performance with that of other classification models. The results, along with one-way ANOVA and Tukey's test, indicated that our proposed model (CNN-LSTM) performed the best according to precision, recall, and f-measure criteria. This suggests that it can be a reliable model for estimating the severity of oral and dental diseases, thereby assisting patients.
Abstract (translated)
分析作者在文本中的情感作为一种识别文本极性的技术在医学和口腔领域是实用和有用的。目前，由于患者对自己的疾病了解有限、难以获得专家医生帮助或害怕生病等原因，特别是在疫情条件下，从收到放射学报告到看医生的时间可能会延迟。在某些情况下，这种延迟会对患者造成严重风险，因此及时做出决策至关重要。开发一个自动系统，根据放射学报告的文本内容告知患者病情恶化程度，可以极大地促进及时决策。 在这项研究中，从 Shiraz University of Medical Sciences 收集了1,134个锥束计算机断层扫描（CBCT）照片报告的数据。对每份文件，专家都会对患者的病情严重程度进行标注。经过预处理所有文本数据后，开发了一个基于卷积神经网络（CNN）和长短时记忆（LSTM）网络架构的深度学习模型，称为 CNN-LSTM，以根据放射学报告中的情感分析患者的病情严重程度。对模型的性能进行了评估，在两种不均衡和两种平衡的场景中进行。最后，为了证明我们模型的有效性，我们将其性能与其它分类模型的性能进行了比较。结果加上单因素方差分析和 Tukey 检验，表明我们的提议模型（CNN-LSTM）根据精度、召回率和 F1 值标准，性能最佳。这表明它可以作为一个可靠模型来估计口腔和牙齿疾病严重程度，从而帮助患者。
URL
https://arxiv.org/abs/2401.12993
