Behavioral Modeling for Mental Health using Machine Learning Algorithms.

Mental health is an indicator of emotional, psychological and social well-being of an individual. It determines how an individual thinks, feels and handle situations. Positive mental health helps one to work productively and realize their full potential. Mental health is important at every stage of life, from childhood and adolescence through adulthood. Many factors contribute to mental health problems which lead to mental illness like stress, social anxiety, depression, obsessive compulsive disorder, drug addiction, and personality disorders. It is becoming increasingly important to determine the onset of the mental illness to maintain proper life balance. The nature of machine learning algorithms and Artificial Intelligence (AI) can be fully harnessed for predicting the onset of mental illness. Such applications when implemented in real time will benefit the society by serving as a monitoring tool for individuals with deviant behavior. This research work proposes to apply various machine learning algorithms such as support vector machines, decision trees, naïve bayes classifier, K-nearest neighbor classifier and logistic regression to identify state of mental health in a target group. The responses obtained from the target group for the designed questionnaire were first subject to unsupervised learning techniques. The labels obtained as a result of clustering were validated by computing the Mean Opinion Score. These cluster labels were then used to build classifiers to predict the mental health of an individual. Population from various groups like high school students, college students and working professionals were considered as target groups. The research presents an analysis of applying the aforementioned machine learning algorithms on the target groups and also suggests directions for future work.

Isolated bilateral mandibular angle fractures: an extensive literature review of the rare clinical phenomenon with presentation of a classical clinical model.

Bilateral angle fractures are a rare clinical phenomenon in contrast to the incidence of unilateral angle fractures. However, the rarity has garnered less attention in spite of the uniqueness of fracture pattern and distinctive biomechanics. This article is a detailed review on the etiology, clinical presentation, and management of bilateral angle fractures with the presentation of an interesting case. The bilateral angle fracture reported is a untreated, malunited fracture representing an ideal clinical model to study its biomechanics. The clinical features were anterior open bite, increased facial height, and temporomandibular joint tenderness. The management included osteotomy at the malunion and miniplate osteosynthesis. Bilateral angle fracture presents mandible in three independent fragments (left angle, right angle, and intermediate corpus), each with strong muscles acting in different vectors. This makes the fracture vulnerable to severe displacing forces and unfavorable to achieve the optimal reduction, stability, and healing. This necessitates comprehension of the biomechanical forces involved to avoid malunion following fixation. The article details the complex biomechanics of mandibular angle and its clinical implications in the rare event of bilateral angle fractures. It describes the necessity for a systematic approach and ideal osteosynthesis principles to achieve maximal treatment outcomes and minimal complications.

Rare foreign bodies in oro-facial regions.

Accidental entry of foreign bodies into the oro-facial region could be due to trauma, therapeutic interventions or iatrogenic. Various foreign bodies and locations have been reported, for example, wood in the orbit, impression material in the maxillary sinus, tooth fragments in the orbit. All these cases presented with inflammatory reaction and formation of infected granuloma, pus discharging sinus and serious complications like intra-cranial abscesses. Foreign bodies sometimes migrate within the tissues and become symptomatic after a certain period of time. In these cases, it is very difficult to correlate the direct relation between the suspected foreign bodies with the present clinical symptoms. The removal of foreign bodies is often a surgical challenge due to a combination of difficulty in access and close anatomical relationship to vital structures. To prevent complications, foreign bodies should be diagnosed and removed on time.