activity
20212024
most citedIdentifying depression-related topics in smartphone-collected free-response speech recordings using an automatic speech recognition system and a deep learning topic model

1 citations · 1 across the 4 of their papers we have counts for

collaborators

4 papers

stat.AP2024

Deciphering seasonal depression variations and interplays between weather changes, physical activity, and depression severity in real-world settings: Learnings from RADAR-MDD longitudinal mobile health study

Yuezhou Zhang, Amos A. Folarin, Yatharth Ranjan +20

Prior research has shown that changes in seasons and weather can have a significant impact on depression severity. However, findings are inconsistent across populations, and the in…

cs.CL20231 cited

Identifying depression-related topics in smartphone-collected free-response speech recordings using an automatic speech recognition system and a deep learning topic model

Yuezhou Zhang, Amos A Folarin, Judith Dineley +25

Language use has been shown to correlate with depression, but large-scale validation is needed. Traditional methods like clinic studies are expensive. So, natural language processi…

cs.CY2023

Disease Insight through Digital Biomarkers Developed by Remotely Collected Wearables and Smartphone Data

Zulqarnain Rashid, Amos A Folarin, Yatharth Ranjan +7

Digital Biomarkers and remote patient monitoring can provide valuable and timely insights into how a patient is coping with their condition (disease progression, treatment response…

q-bio.QM2021

The utility of wearable devices in assessing ambulatory impairments of people with multiple sclerosis in free-living conditions

Shaoxiong Sun, Amos A Folarin, Yuezhou Zhang +25

Multiple sclerosis (MS) is a progressive inflammatory and neurodegenerative disease of the central nervous system affecting over 2.5 million people globally. In-clinic six-minute w…