Early Detection of Depression Based on Linguistic Metadata Augmented Classifiers Revisited

Trotzek, Marcel; Koitka, Sven; Friedrich, Christoph M.

doi:10.1007/978-3-319-98932-7_18

Cited by 4 publications

(1 citation statement)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Specifically, we experiment with a Logistic Regression model (Cox, 1958), a Random Forest model (Ho, n.d.) and an Extreme Gradient Boosting model (T. Chen & Guestrin, 2016) (see, for example, X. Chen et al, 2018;Trotzek et al, 2017). The models are trained with default hyperparameters 5 , and the classification threshold is set for the subjects in the same fashion as in MentalBERT.…”

Section: Baseline Modelsmentioning

confidence: 99%

Early Depression Detection with Transformer Models: Analyzing the Relationship between Linguistic and Psychology-Based Features

Halimeh,

Caron,

Müller

2023

Proceedings of the Annual Hawaii International Conference on System Sciences

View full text Add to dashboard Cite

Clinical depression is a serious mental disorder that poses challenges for both personal and public health.Millions of people struggle with depression each year, but for many, the disorder goes undiagnosed or untreated. Over the last decade, early depression detection on social media emerged as an interdisciplinary research field. However, there is still a gap in detecting hesitant, depression-susceptible individuals with minimal direct depressive signals at an early stage. We, therefore, take up this open point and leverage posts from Reddit to fill the addressed gap. Our results demonstrate the potential of contemporary Transformer architectures in yielding promising predictive capabilities for mental health research. Furthermore, we investigate the model's interpretability using a surrogate and a topic modeling approach. Based on our findings, we consider this work as a further step towards developing a better understanding of mental eHealth and hope that our results can support the development of future technologies.

show abstract

Section: Baseline Modelsmentioning

confidence: 99%