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Early detection of burnout is of utmost importance to avoid severe health consequences. Burnout is typically assessed through standardized questionnaires with self-reported information, a technique that could potentially delay its diagnosis. Wearable devices continuously and unobtrusively collect health-related data, making them valuable tools for the early detection of several mental health issues, including burnout syndrome. In this paper we report initial insights on the machine learning prediction of baseline burnout risk across cognitive, emotional, and physical dimensions. Our data consists of the first 30 days of a 9-months longitudinal study with 239 participants, including monthly burnout assessments and health data from smartwatches. Aggregated mean and standard deviation of physiological features over time windows of varying duration were employed as predictors of baseline burnout risk. Models employing sleep, cardiac, and stress features achieved a balanced accuracy of 0.66 and 0.68 in the detection of cognitive weariness and physical fatigue risk, respectively. The prediction of emotional exhaustion risk reached lower performance with a balanced accuracy of 0.55, suggesting the need of integrating additional data sources to reach better-than-chance performance. We expect to improve burnout risk prediction by crafting additional features and exploiting the collected data over their full longitudinal scale.