A dynamic prediction model for predicting the time at which patients with MCI progress to AD based on time-dependent covariates
Abstract Background Alzheimer’s Disease (AD) is an irreversible neurodegenerative disorder that imposes a significant burden on families and society. Timely intervention during the transitional stages from Mild Cognitive Impairment (MCI) to AD can help mitigate this issue. The MCI-to-AD conversion t...
Saved in:
| Main Authors: | Yanjie Wang, Yu Song, Chengfeng Zhang, Jiaqiao Ren, Pansheng Xue, Yawen Hou, Zheng Chen |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-07-01
|
| Series: | BMC Medical Informatics and Decision Making |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12911-025-03040-5 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Covariational reasoning of field dependent prospective mathematics teacher in solving covariance problem
by: Fatchiyah Rahman, et al.
Published: (2023-07-01) -
IoT-edge anomaly detection for covariate shifted and point time series health data
by: Partha Pratim Ray, et al.
Published: (2022-11-01) -
Comparison of the Efficacy of Different Exercise Modes on MCI Adults: A Network Meta‐Analysis
by: Changjiang Qiu, et al.
Published: (2025-08-01) -
Deep learning models for the analysis of high-dimensional survival data with time-varying covariates while handling missing data
by: Sarah Ogutu, et al.
Published: (2025-07-01) -
Restricted mean survival time approach versus time-varying coefficient Cox model for quantifying treatment effect when hazards are non-proportional
by: Tianyuan Gu, et al.
Published: (2025-07-01)