 |
|
Development of a Frailty Detection Model Using Machine Learning with the Korean Frailty and Aging Cohort Study Data
Dongjun Koo, Ah Ra Lee, Eunjoo Lee, Il Kon Kim
Healthc Inform Res. 2022;28(3):231-239. Published online 2022 Jul 31 DOI: https://doi.org/10.4258/hir.2022.28.3.231
|
|
Citations to this article as recorded by
 Predicting Obstructive Sleep Apnea Based on Computed Tomography Scans Using Deep Learning Models
Jeong-Whun Kim, Kyungsu Lee, Hyun Jik Kim, Hae Chan Park, Jae Youn Hwang, Seok-Won Park, Hyoun-Joong Kong, Jin Youp Kim
American Journal of Respiratory and Critical Care Medicine.2024; 210(2): 211. CrossRef Digital health technology combining wearable gait sensors and machine learning improve the accuracy in prediction of frailty
Shaoyi Fan, Jieshun Ye, Qing Xu, Runxin Peng, Bin Hu, Zhong Pei, Zhimin Yang, Fuping Xu
Frontiers in Public Health.2023;[Epub] CrossRef Machine learning approaches for frailty detection, prediction and classification in elderly people: A systematic review
Matteo Leghissa, Álvaro Carrera, Carlos A. Iglesias
International Journal of Medical Informatics.2023; 178: 105172. CrossRef Benefits of Information Technology in Healthcare: Artificial Intelligence, Internet of Things, and Personal Health Records
Hyejung Chang, Jae-Young Choi, Jaesun Shim, Mihui Kim, Mona Choi
Healthcare Informatics Research.2023; 29(4): 323. CrossRef
|