Digital biomarkers for depression screening with wearable devices: cross-sectional study with machine learning modeling
Background: Depression is a prevalent mental disorder that is undiagnosed and untreated in half of all cases. Wearable activity trackers collect fine-grained sensor data characterizing the behavior and physiology of users (ie, digital biomarkers), which could be used for timely, unobtrusive, and sc...
Main Authors: | Rykov, Yuri, Thach, Thuan-Quoc, Bojic, Iva, Christopoulos, George, Car, Josip |
---|---|
Other Authors: | Lee Kong Chian School of Medicine (LKCMedicine) |
Format: | Journal Article |
Language: | English |
Published: |
2022
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/153936 |
Similar Items
-
Digital Biomarkers for Depression Screening With Wearable Devices: Cross-sectional Study With Machine Learning Modeling
by: Yuri Rykov, et al.
Published: (2021-10-01) -
Advancing translational research through the interface of digital phenotyping and neuroimaging: A narrative review
by: Erica Camacho, et al.
Published: (2021-06-01) -
IRGM promotes melanoma cell survival through autophagy and is a promising prognostic biomarker for clinical application
by: Linlu Tian, et al.
Published: (2021-03-01) -
Biomakers in Chronic Chagas Cardiomyopathy
by: Angela Braga Rodrigues, et al.
Published: (2022-08-01) -
Editorial: Prognostic biomarkers for oral cancer
by: Sabrina Wurzba, et al.
Published: (2022-09-01)