• Journal of Oral Medicine and Pain
• /
• v.33 no.3
• /
• pp.279-294
• /
• 2008

This study aimed to make an analysis of the occlusion in the state of muscle fatigue produced by excessive mouth opening and clenching during the dental treatment to control the dental pain and to evaluate the sensory nerve in the muscle pain state. Most of the reasons why patients visit the dental office result in pain-either conceivably the dental origin pain or the non-dental origin pain. The dental offices have many therapeutic actions to produce the masticatory muscle fatigue for the treatment. Dental treatment with long minutes of mouth opening can cause some headaches, masticatory muscle pain and mouth opening difficulties. Patients with mastication problems who visits a dental office to alleviate pain run against another unexpected pain with other aspects. This study uses T-scan II system(Tekscan Co., USA) for the evaluation on the occlusal pattern in the experimental muscle fatigue after clenching, opening the mouth excessively and chewing gum. The occlusal contact pattern is analyzed by the contact timing, namely first, intercuspal, maximum and end point of contact. This inspection was performed at frequencies of 2000Hz, 250 Hz and 5 Hz before and after each experimental muscle pain was produced to 24 subjects who had normal occlusion without the orthodontic treatment or a wide range of the prosthesis by using $neurometer^{\circledR}$ CPT/C(Neurotron, Inc. Baltimore, Maryland, USA). The measuring sites were mandibular nerve experimental muscle fatigue respectively. This study could obtain the following results after the assessment of occlusion and sensory nerve of the experimental muscle fatigue. 1. There were the fastest expression after the excessive mouth opening in muscle fatigue and after tooth clenching in muscle pain. In the visual analog scale that records the subjective level, there was the highest scale after the clenching in the muscle fatigue in jumping off the point of pain. 2. Tooth contact time, contact force, relative contact force on the point of the first contact had no difference, and there were decreases in the contact force after the excessive mouth opening on intercuspal position point, after the excessive mouth opening and the gum chewing on the point of the maximum, and in the contact time after all the experimental muscle fatigue state on the point of the end contact. 3. There was no statistic significance in the current perception threshold before and after the experimental muscle fatigue. 4. There was no significant difference in the contact number, the maximal contact number on the point of the first contact, and the contact number after the mouth opening and gum chewing on the point of the intercuspal position and the contact number after the experimental muscle fatigue on the maximum point, and showed significant decreases. In conclusion, it was found that the occlusal pattern can cause the changes on the case of the clinical muscle weakness by intra-external oral events. It was important that the sedulous attention to details is required during dental treatment in case of excessive mouth opening, mastication and clenching.

• Journal of Intelligence and Information Systems
• /
• v.21 no.3
• /
• pp.1-17
• /
• 2015

In this paper, we report what we have observed with regards to a prediction model for the military based on enlisted men's internal(cumulative records) and external data(SNS data). This work is significant in the military's efforts to supervise them. In spite of their effort, many commanders have failed to prevent accidents by their subordinates. One of the important duties of officers' work is to take care of their subordinates in prevention unexpected accidents. However, it is hard to prevent accidents so we must attempt to determine a proper method. Our motivation for presenting this paper is to mate it possible to predict accidents using enlisted men's internal and external data. The biggest issue facing the military is the occurrence of accidents by enlisted men related to maladjustment and the relaxation of military discipline. The core method of preventing accidents by soldiers is to identify problems and manage them quickly. Commanders predict accidents by interviewing their soldiers and observing their surroundings. It requires considerable time and effort and results in a significant difference depending on the capabilities of the commanders. In this paper, we seek to predict accidents with objective data which can easily be obtained. Recently, records of enlisted men as well as SNS communication between commanders and soldiers, make it possible to predict and prevent accidents. This paper concerns the application of data mining to identify their interests, predict accidents and make use of internal and external data (SNS). We propose both a topic analysis and decision tree method. The study is conducted in two steps. First, topic analysis is conducted through the SNS of enlisted men. Second, the decision tree method is used to analyze the internal data with the results of the first analysis. The dependent variable for these analysis is the presence of any accidents. In order to analyze their SNS, we require tools such as text mining and topic analysis. We used SAS Enterprise Miner 12.1, which provides a text miner module. Our approach for finding their interests is composed of three main phases; collecting, topic analysis, and converting topic analysis results into points for using independent variables. In the first phase, we collect enlisted men's SNS data by commender's ID. After gathering unstructured SNS data, the topic analysis phase extracts issues from them. For simplicity, 5 topics(vacation, friends, stress, training, and sports) are extracted from 20,000 articles. In the third phase, using these 5 topics, we quantify them as personal points. After quantifying their topic, we include these results in independent variables which are composed of 15 internal data sets. Then, we make two decision trees. The first tree is composed of their internal data only. The second tree is composed of their external data(SNS) as well as their internal data. After that, we compare the results of misclassification from SAS E-miner. The first model's misclassification is 12.1%. On the other hand, second model's misclassification is 7.8%. This method predicts accidents with an accuracy of approximately 92%. The gap of the two models is 4.3%. Finally, we test if the difference between them is meaningful or not, using the McNemar test. The result of test is considered relevant.(p-value : 0.0003) This study has two limitations. First, the results of the experiments cannot be generalized, mainly because the experiment is limited to a small number of enlisted men's data. Additionally, various independent variables used in the decision tree model are used as categorical variables instead of continuous variables. So it suffers a loss of information. In spite of extensive efforts to provide prediction models for the military, commanders' predictions are accurate only when they have sufficient data about their subordinates. Our proposed methodology can provide support to decision-making in the military. This study is expected to contribute to the prevention of accidents in the military based on scientific analysis of enlisted men and proper management of them.