Background In the older general population, neurodegenerative diseases (NDs) are
associated with increased disability, decreased physical and cognitive function. Detecting …

Background and objective Alzheimer's disease (AD) is the most common type of dementia
that can seriously affect a person's ability to perform daily activities. Estimates indicate that …

Early identification of individuals at risk of developing Alzheimer's disease (AD) dementia is
important for developing disease-modifying therapies. In this study, given multimodal AD …

Artificial intelligence (AI) approaches pose a great opportunity for individualized, pre-
symptomatic disease diagnosis which plays a key role in the context of personalized …

Introduction This study aims to explore machine learning (ML) methods for early prediction
of Alzheimer's disease (AD) and related dementias (ADRD) using the real‐world electronic …

Objective The purpose of this study was to develop and validate a predictive model of
cognitive impairment in older adults based on a novel machine learning (ML) algorithm …

Background: With the increase in the world's aging population, there is a growing need to
prevent and predict dementia among the general population. The availability of national time …

Objective We develop a deep learning framework based on the pre-trained Bidirectional
Encoder Representations from Transformers (BERT) model using unstructured clinical notes …

Background Since no effective therapies exist for Alzheimer's disease (AD), prevention has
become more critical through lifestyle status changes and interventions. Analyzing electronic …

Background The polygenic nature of Alzheimer's disease (AD) suggests that multiple
variants jointly contribute to disease susceptibility. As an individual's genetic variants are …