Estimating the severity of dental and oral problems via sentiment classification over clinical reports

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.