Machine Learning Predictive Models Can Improve Efficacy of Clinical Trials for Alzheimer's Disease.
Journal:
Journal of Alzheimer's disease : JAD
PMID:
31985462
Abstract
BACKGROUND: The ideal participants for Alzheimer's disease (AD) clinical trials would show cognitive decline in the absence of treatment (i.e., placebo arm) and also would be responsive to the therapeutic intervention being studied (i.e., drug arm). One strategy to boost the power of trials is to enroll individuals who are more likely to progress targeted using data-driven predictive models.
Authors
Keywords
Aged
Aged, 80 and over
Algorithms
Alzheimer Disease
Clinical Trials as Topic
Clinical Trials, Phase III as Topic
Cognitive Dysfunction
Databases, Factual
Female
Follow-Up Studies
Humans
Machine Learning
Magnetic Resonance Imaging
Male
Mental Status and Dementia Tests
Neuroimaging
Predictive Value of Tests
Reproducibility of Results
Research Design
Sample Size
Sensitivity and Specificity