A review of machine learning in obesity.

Rich sources of obesity-related data arising from sensors, smartphone apps, electronic medical health records and insurance data can bring new insights for understanding, preventing and treating obesity. For such large datasets, machine learning provides sophisticated and elegant tools to describe, classify and predict obesity-related risks and outcomes. Here, we review machine learning methods that predict and/or classify such as linear and logistic regression, artificial neural networks, deep learning and decision tree analysis.

Litigation and chronic facial pain.

Clinical studies have suggested that the presence of litigation in chronic pain syndromes may complicate diagnostic and treatment strategies. In addition, psychosocial factors may be prevalent in such cases. The present study explored the possible correlation in the facial pain population between patients in litigation and psychological disturbance as measured by the Minnesota Multiphasic Personality Inventory.

Comparison of MMPI scores with self-report of sleep disturbance and bruxism in the facial pain population.

Sleep disturbance and bruxism are common clinical characteristics of the chronic facial pain patient. Previous studies have shown that chronic pain patients reporting disturbed sleep show more psychopathology and respond less readily to treatment. Bruxism has been linked to emotional stress and periods of difficult life change.