Alzheimer's Disease

Development of Alzheimer’s Disease Risk Score for Future Primary Care: A White-Box Approach

Interpretable scoring system can contribute to bridge the gap between the timeliness and complexity of diagnosing Alzheimer’s disease (AD) and promote early intervention at non-specialist settings. To develop a risk score to predict the likelihood of AD with interpretable machine learning using variables that are obtainable at integrated primary care settings. A secondary data analysis including c...

Machine learning-based prediction of future dementia using routine clinical MRI brain scans and healthcare data

Early identification of dementia risk is essential for preventive care and timely enrolment into disease-modifying interventions. Current approaches rely on costly, invasive, or research-only methods not feasible at scale within public health systems. To test whether routinely acquired NHS brain MRI scans can be used to predict future dementia diagnosis and whether confidence-based stratification ...

Predicting Alzheimer’s Disease Diagnosis, a Decade or more Years before Onset using the Electronic Health Record and Random Forest Machine Learning Models

There is need to detect and intervene in pre-clinical phases of Alzheimer’s disease (AD). Electronic health records (EHRs) may help predict AD using m...

Integrating Infection Burden and Multimodal Biomarkers for Early Detection of Alzheimers Disease: A Sheaf-ML Framework

Alzheimers disease (AD) remains a major global health challenge, with growing evidence linking chronic infections, immune aging, and neurodegeneration...

Neuroimaging-derived brain endophenotypes link molecular mechanisms to Alzheimer’s disease and aging

Alzheimer’s disease (AD) genome-wide association studies (GWAS), typically based on clinical phenotypes, have identified numerous risk loci, yet linki...

A Large-Scale Serum Metabolite Panel for Baseline Detection of Alzheimer’s Disease

Blood-based metabolomic signatures offer promising, non-invasive avenues for Alzheimer’s disease (AD) detection. We aimed to identify a serum metaboli...

Large Language Model-Driven Prioritization of Alzheimer’s Disease Drug Targets Across Multidimensional Criteria

Large language models (LLMs) offer new opportunities to synthesize the vast and heterogeneous biomedical literature, yet their potential to support dr...

Relationship of the Microbiome with Neurodegenerative Diseases: Development of AI Tools to Detect Alzheimer’s

This study aimed to develop an artificial intelligence (AI) algorithm capable of distinguishing Alzheimer’s disease (AD) from healthy patients using g...

Acoustic Analysis of Primary Care Patient-provider Conversations to Screen for Cognitive Impairment

Cognitive impairment (CI) is often under detected in primary care due to time and resource constraints. Passive analysis of clinical dialogue may offe...

A foundation model for mapping the phenomic and genetic landscape of cerebral small vessel disease biomarkers

Cerebral small vessel disease (CSVD) is a leading cause of age-related cognitive decline and neurological disorders, yet its precise characterization ...

Clustering high-cost patients in England using machine learning: a population-based cohort study

To identify clusters of high-cost patients in England based on diagnoses and sociodemographic characteristics to inform targeted population health man...

A Pathway-Based Machine Learning Approach Identifies Region-Specific Markers and Patterns in Alzheimer’s Disease Patients Based on Spatial and Severity Metadata

Alzheimer’s disease (AD) exhibits profound spatial heterogeneity in its molecular and pathological features, yet the basis of this regional selectivit...

Machine Learning-Based Prediction of Cell-type Resolved Brain eQTLs Enhances Discovery of Variants Explaining Alzheimer’s Disease Heritability

The majority of causal genome-wide association studies (GWAS) variants for Alzheimer’s disease (AD) are believed to reside in noncoding regions of the...

Multiscale-Multistage Temporal Convolutional Network for EEG-Based Mild Cognitive Impairment Detection

Mild cognitive impairment (MCI) is an intermediate stage between normal ageing and dementia, with affected individuals at a higher risk of progressing...

Personalized Data-Driven Robust Machine Learning Models to Differentiate Parkinson’s Disease Patients Using Heterogeneous Risk Factors

Parkinson’s Disease (PD) is the most prevalent neurodegenerative disorder after Alzheimer’s, yet its diagnosis largely relies on subjective clinical a...

Antidepressant Use at the Threshold: using electronic health records to characterise people prescribed antidepressants around the time of dementia diagnosis

Antidepressant use is common in people with dementia. Antidepressants may be started to manage symptoms of dementia, rather than depressive and anxiet...

A Comprehensive Investigation of Machine Learning Practices in Predictive Modeling of Alzheimer’s Disease and Related Dementias using Multisite Real-world Electronic Health Records

The irreversible progression and profound societal impact of Alzheimer’s disease and related dementias (AD/ADRD) underscore the pressing need for earl...

CLIN-SUMM: Temporal Summarization of Longitudinal Clinical Notes

Electronic health records (EHRs) contain years of longitudinal clinical notes that capture evolving patient health, treatments, and outcomes. However,...

Brain Region-Centered MultiModal Hypergraph Fusion for MCI Conversion Prediction

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder, with mild cognitive impairment (MCI) as its prodromal stage. Accurate MCI conver...

Dementia etiology classification using NULISA plasma biomarkers and machine learning

Accurate antemortem differentiation among dementia etiologies remains challenging, particularly for atypical or mixed clinical presentations. Multiple...