ArticleNature communications2022
Deep phenotyping of Alzheimer's disease leveraging electronic medical records identifies sex-specific clinical associations.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
34 citing papers in PubMed, 1 synthesis or guideline pooled it, 39 citations in OpenAlex.
- Demographic reporting across a decade of neuroimaging: a systematic review.Brain imaging and behavior · 2022Pooled it
- Article
- Leveraging Advanced AI Frameworks for Dual PPAR α/γ Agonist Discovery in Alzheimer's Disease.ACS chemical neuroscience · 2026Review
- Identifying sex-specific sub-phenotypes of Alzheimer's disease progression using longitudinal electronic health records.EBioMedicine · 2026Article
- A phenotype-to-mechanism framework links phenome-wide comorbidity architecture to molecular mechanisms and therapeutic discovery in complex diseases.medRxiv : the preprint server for health sciences · 2026Article
- PhenoNMF: A novel multi-layer matrix factorization framework for age-stratified comprehensive phenotypic similarity analysis.BMC medical informatics and decision making · 2026Article
- Stratification of Alzheimer's disease patients using knowledge-guided unsupervised latent factor clustering with electronic health record data.Communications medicine · 2026Article
- Addressing the diagnostic gap through deep phenotyping.Human genomics · 2026Article
- Tau topography subtypes account for clinical heterogeneity and longitudinal trajectories in early-onset Alzheimer's disease.Brain communications · 2026Article
- Predicting Alzheimer's Disease Diagnosis, a Decade or more Years before Onset using the Electronic Health Record and Random Forest Machine Learning Models.medRxiv : the preprint server for health sciences · 2025Article
- Characterizing Dementia Phenotypes from Unstructured EHR Notes with Generative AI and Interpretable Machine Learning.medRxiv : the preprint server for health sciences · 2025Article
- Decoding subphenotypes in electronic medical records within late-onset Alzheimer's disease reveals heterogeneity and sex-specific differences.medRxiv : the preprint server for health sciences · 2025Article
- Leveraging electronic health records from two hospital systems identifies male infertility-associated comorbidities across time.Communications medicine · 2025Article
- Identifying Sex-Specific Sub-phenotypes of Alzheimer's Disease Progression Using Longitudinal Electronic Health Records.medRxiv : the preprint server for health sciences · 2025Article
- Comorbidities associated with a clinically-recognized delirium diagnosis in the hospital using real world data.Communications medicine · 2025Article
- Few shot learning for phenotype-driven diagnosis of patients with rare genetic diseases.NPJ digital medicine · 2025Article
- Article
- Predicting explainable dementia types with LLM-aided feature engineering.Bioinformatics (Oxford, England) · 2025Article
- Uncovering Important Diagnostic Features for Alzheimer's, Parkinson's and Other Dementias Using Interpretable Association Mining Methods.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2025Article
- Determining the Importance of Clinical Modalities for NeuroDegenerative Disorders and Risk of Patient Injury Using Machine Learning and Survival Analysis.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
Corrections and comments
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Authors and funding
14 authors at 3 institutions in 1 country.
Funding
Abstract
Alzheimer's Disease (AD) is a neurodegenerative disorder that is still not fully understood. Sex modifies AD vulnerability, but the reasons for this are largely unknown. We utilize two independent electronic medical record (EMR) systems across 44,288 patients to perform deep clinical phenotyping and network analysis to gain insight into clinical characteristics and sex-specific clinical associations in AD. Embeddings and network representation of patient diagnoses demonstrate greater comorbidity interactions in AD in comparison to matched controls. Enrichment analysis identifies multiple known and new diagnostic, medication, and lab result associations across the whole cohort and in a sex-stratified analysis. With this data-driven method of phenotyping, we can represent AD complexity and generate hypotheses of clinical factors that can be followed-up for further diagnostic and predictive analyses, mechanistic understanding, or drug repurposing and therapeutic approaches.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.