Article

Identifying Sex-Specific Sub-phenotypes of Alzheimer’s Disease Progression Using Longitudinal Electronic Health Records

2024-07-08

Abstract excerpt

Alzheimer’s Disease (AD) is a complex neurodegenerative disorder strongly influenced by sex differences, with women comprising nearly two-thirds of cases. However, sex-specific progression patterns remain underexplored due to unclear clinical and molecular mechanisms. To address this gap, we developed a temporal autoencoder framework to identify sex-specific AD sub-phenotypes using longitudinal electronic health r...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 57db670a-3c47-5ae2-bbc2-9db3780dfc96
DOI: 10.1101/2024.07.07.24310055

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Identifying Sex-Specific Sub-phenotypes of Alzheimer’s Disease Progression Using Longitudinal Electronic Health RecordsDOI 10.1101/2024.07.07.24310055Read

Identifying sex-specific sub-phenotypes of Alzheimer's disease progression using longitudinal electronic health records.Semantic proximity Read

Deep clinical phenotyping of Alzheimer’s Disease Patients Leveraging Electronic Medical Records Data Identifies Sex-Specific Clinical AssociationsSemantic proximity Read

Deep phenotyping of Alzheimer's disease leveraging electronic medical records identifies sex-specific clinical associations.Semantic proximity Read

Identifying progression subphenotypes of Alzheimer's disease from large-scale electronic health records with machine learning.Semantic proximity Read

Unraveling sex differences in Alzheimer's disease and related endophenotypes with brain proteomesSemantic proximity Read

Sex Differences in the Alzheimer’s Brain Age Gap: APOE ε4 Plays a Major RoleSemantic proximity Read

Improved polygenic risk prediction for alzheimer's disease and related dementias using deep learning: age and APOE-stratified analysis.Semantic proximity Read

Integrative Machine Learning Approach to Risk Prediction for Dementia and Alzheimer’s DiseaseSemantic proximity Read

Predicting Alzheimer’s Trajectory: A Multi-PRS Machine Learning Approach for Early Diagnosis and Progression ForecastingSemantic proximity Read

Unified AI framework to uncover deep interrelationships between gene expression and Alzheimer’s disease neuropathologiesSemantic proximity Read

Imaging-genetics-based dementia risk prediction using deep survival neural networks in the Rotterdam StudySemantic proximity Read

Sex specific molecular networks and key drivers of Alzheimer’s diseaseSemantic proximity Read