The Alzheimer's Disease Neuroimaging Initiative in the era of Alzheimer's disease treatment: A review of ADNI studies from 2021 to 2022
DP Veitch, MW Weiner, M Miller, PS Aisen… - Alzheimer's & …, 2024 - Wiley Online Library
Abstract The Alzheimer's Disease Neuroimaging Initiative (ADNI) aims to improve
Alzheimer's disease (AD) clinical trials. Since 2006, ADNI has shared clinical, neuroimaging …
Alzheimer's disease (AD) clinical trials. Since 2006, ADNI has shared clinical, neuroimaging …
[PDF][PDF] Neurosymbolic ai for reasoning on graph structures: A survey
Neurosymbolic AI is an increasingly active area of research which aims to combine symbolic
reasoning methods with deep learning to generate models with both high predictive …
reasoning methods with deep learning to generate models with both high predictive …
[HTML][HTML] Semantic harmonization of Alzheimer's disease datasets using AD-Mapper
Background: Despite numerous past endeavors for the semantic harmonization of
Alzheimer's disease (AD) cohort studies, an automatic tool has yet to be developed …
Alzheimer's disease (AD) cohort studies, an automatic tool has yet to be developed …
[HTML][HTML] An ontology-based approach for harmonization and cross-cohort query of Alzheimer's disease data resources
Abstract Background In the United States, the National Alzheimer's Coordinating Center
(NACC) and the Alzheimer's Disease Neuroimaging Initiative (ADNI) are two major data …
(NACC) and the Alzheimer's Disease Neuroimaging Initiative (ADNI) are two major data …
[HTML][HTML] Generation of realistic synthetic data using multimodal neural ordinary differential equations
P Wendland, C Birkenbihl, M Gomez-Freixa… - NPJ Digital …, 2022 - nature.com
Individual organizations, such as hospitals, pharmaceutical companies, and health
insurance providers, are currently limited in their ability to collect data that are fully …
insurance providers, are currently limited in their ability to collect data that are fully …
[HTML][HTML] psHarmonize: Facilitating reproducible large-scale pre-statistical data harmonization and documentation in R
JJ Stephen, P Carolan, AE Krefman, S Sedaghat… - Patterns, 2024 - cell.com
Combining pertinent data from multiple studies can increase the robustness of
epidemiological investigations. Effective" pre-statistical" data harmonization is paramount to …
epidemiological investigations. Effective" pre-statistical" data harmonization is paramount to …
Neurosymbolic AI for Reasoning Over Knowledge Graphs: A Survey
Neurosymbolic artificial intelligence (AI) is an increasingly active area of research that
combines symbolic reasoning methods with deep learning to leverage their complementary …
combines symbolic reasoning methods with deep learning to leverage their complementary …
[HTML][HTML] Comparison and aggregation of event sequences across ten cohorts to describe the consensus biomarker evolution in Alzheimer's disease
S Golriz Khatami, Y Salimi, M Hofmann-Apitius… - Alzheimer's Research & …, 2022 - Springer
Abstract Background Previous models of Alzheimer's disease (AD) progression were
primarily hypothetical or based on data originating from single cohort studies. However …
primarily hypothetical or based on data originating from single cohort studies. However …
Deep learning-based patient stratification for prognostic enrichment of clinical dementia trials
Dementia probably due to Alzheimer's disease (AD) is a progressive condition that
manifests in cognitive decline and impairs patients' daily life. Affected patients show great …
manifests in cognitive decline and impairs patients' daily life. Affected patients show great …
Simulation-based power analysis could improve the design of clinical trials in Alzheimer's disease
Clinical trials of new treatments in different progressive diseases use power analysis to
determine the sample size needed for a trial to obtain a statistically significant estimate for …
determine the sample size needed for a trial to obtain a statistically significant estimate for …