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

Merhbene, Ghofrane; Nath, Sukanya; Puttick, Alexandre Riemann; Kurpicz-Briki, Mascha (2022). BurnoutEnsemble: Augmented Intelligence to Detect Indications for Burnout in Clinical Psychology Frontiers in Big Data, 5(2022) Frontiers 10.3389/fdata.2022.863100

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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.

Item Type:	Journal Article (Original Article)
Division/Institute:	School of Engineering and Computer Science > Institute for Data Applications and Security (IDAS) School of Engineering and Computer Science > Institute for Data Applications and Security (IDAS) > IDAS / Applied Machine Intelligence School of Engineering and Computer Science
Name:	Merhbene, Ghofrane; Nath, Sukanya; Puttick, Alexandre Riemann and Kurpicz-Briki, Mascha0000-0001-5539-6370
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software
ISSN:	2624-909X
Publisher:	Frontiers
Funders:	[7] Swiss National Science Foundation
Language:	English
Submitter:	Mascha Kurpicz-Briki
Date Deposited:	30 May 2022 10:03
Last Modified:	30 May 2022 10:03
Publisher DOI:	10.3389/fdata.2022.863100
ARBOR DOI:	10.24451/arbor.16997
URI:	https://arbor.bfh.ch/id/eprint/16997