BurnoutEnsemble: Augmented Intelligence to Detect Indications for Burnout in Clinical Psychology

Abstract

Burnout, a state of emotional, physical, and mental exhaustion caused by excessive and prolonged stress, is a growing concern. It is known to occur when an individual feels overwhelmed, emotionally exhausted, and unable to meet the constant demands imposed upon them. Detecting burnout is not an easy task, in large part because symptoms can overlap with those of other illnesses or syndromes. The use of natural language processing (NLP) methods has the potential to mitigate the limitations of typical burnout detection via inventories. In this article, the performance of NLP methods on anonymized free text data samples collected from the online forum/social media platform Reddit was analyzed. A dataset consisting of 13,568 samples describing first-hand experiences, of which 352 are related to burnout and 979 to depression, was compiled. This work demonstrates the effectiveness of NLP and machine learning methods in detecting indicators for burnout. Finally, it improves upon standard baseline classifiers by building and training an ensemble classifier using two methods (subreddit and random batching). The best ensemble models attain a balanced accuracy of 0.93, test F1 score of 0.43, and test recall of 0.93. Both the subreddit and random batching ensembles outperform the single classifier baselines in the experimental setup.