A multimodal deep learning approach for the prediction of cognitive decline and its effectiveness in clinical trials for Alzheimer's disease.
Transl Psychiatry
; 14(1): 105, 2024 Feb 21.
Article
en En
| MEDLINE
| ID: mdl-38383536
ABSTRACT
Alzheimer's disease is one of the most important health-care challenges in the world. For decades, numerous efforts have been made to develop therapeutics for Alzheimer's disease, but most clinical trials have failed to show significant treatment effects on slowing or halting cognitive decline. Among several challenges in such trials, one recently noticed but unsolved is biased allocation of fast and slow cognitive decliners to treatment and placebo groups during randomization caused by the large individual variation in the speed of cognitive decline. This allocation bias directly results in either over- or underestimation of the treatment effect from the outcome of the trial. In this study, we propose a stratified randomization method using the degree of cognitive decline predicted by an artificial intelligence model as a stratification index to suppress the allocation bias in randomization and evaluate its effectiveness by simulation using ADNI data set.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Contexto en salud:
1_ASSA2030
Problema de salud:
1_doencas_nao_transmissiveis
Asunto principal:
Enfermedad de Alzheimer
/
Disfunción Cognitiva
/
Aprendizaje Profundo
Límite:
Humans
Idioma:
En
Revista:
Transl Psychiatry
Año:
2024
Tipo del documento:
Article
País de afiliación:
Japón