Using Open-Ended Stressor Responses to Predict Depressive Symptoms across Demographics

Autor:	Aguirre, Carlos, Dredze, Mark, Resnik, Philip
Rok vydání:	2022
Předmět:	Computer Science - Computation and Language
Druh dokumentu:	Working Paper
Popis:	Stressors are related to depression, but this relationship is complex. We investigate the relationship between open-ended text responses about stressors and depressive symptoms across gender and racial/ethnic groups. First, we use topic models and other NLP tools to find thematic and vocabulary differences when reporting stressors across demographic groups. We train language models using self-reported stressors to predict depressive symptoms, finding a relationship between stressors and depression. Finally, we find that differences in stressors translate to downstream performance differences across demographic groups. Comment: Extended Abstract presented at Machine Learning for Health (ML4H) symposium 2022, November 28th, 2022, New Orleans, United States & Virtual, http://www.ml4h.cc, 6 pages
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2211.07932 Zobrazit plný text záznamu View this record from Arxiv