3 citations · 9 across the 7 of their papers we have counts for
6 papers · 1 filter
CARMA: Comprehensive Automatically-annotated Reddit Mental Health Dataset for Arabic
Saad Mankarious, Ayah Zirikly
Mental health disorders affect millions worldwide, yet early detection remains a major challenge, particularly for Arabic-speaking populations where resources are limited and menta…
Are Clinical T5 Models Better for Clinical Text?
Yahan Li, Keith Harrigian, Ayah Zirikly +1
Large language models with a transformer-based encoder/decoder architecture, such as T5, have become standard platforms for supervised tasks. To bring these technologies to the cli…
Improving the Generalizability of Depression Detection by Leveraging Clinical Questionnaires
Thong Nguyen, Andrew Yates, Ayah Zirikly +2
Automated methods have been widely used to identify and analyze mental health conditions (e.g., depression) from various sources of information, including social media. Yet, deploy…
Classifying the reported ability in clinical mobility descriptions
Denis Newman-Griffis, Ayah Zirikly, Guy Divita +1
Assessing how individuals perform different activities is key information for modeling health states of individuals and populations. Descriptions of activity performance in clinica…
RSDD-Time: Temporal Annotation of Self-Reported Mental Health Diagnoses
Sean MacAvaney, Bart Desmet, Arman Cohan +4
Self-reported diagnosis statements have been widely employed in studying language related to mental health in social media. However, existing research has largely ignored the tempo…
Embedding Transfer for Low-Resource Medical Named Entity Recognition: A Case Study on Patient Mobility
Denis Newman-Griffis, Ayah Zirikly
Functioning is gaining recognition as an important indicator of global health, but remains under-studied in medical natural language processing research. We present the first analy…