ArticlemedRxiv : the preprint server for health sciences2024
Stratification of Alzheimer's Disease Patients Using Knowledge-Guided Unsupervised Latent Factor Clustering with Electronic Health Record Data.
Article in medRxiv : the preprint server for health sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: People with Alzheimer's disease (AD) exhibit varying clinical trajectories. There is a need to predict future AD-related outcomes such as morbidity and mortality using clinical profile at the point of care. Objective: To stratify AD patients based on baseline clinical profiles (up to two years prior to AD diagnosis) and update the model after AD diagnosis to prognosticate future AD-related outcomes. Methods: Using the electronic health record (EHR) data of a large healthcare system (2011-2022), we first identified patients with ≥1 diagnosis code for AD or related dementia and applied a validated unsupervised phenotyping algorithm to assign AD diagnosis status. Next, we applied an unsupervised latent factor clustering approach, guided by knowledge graph embeddings of relevant EHR features up to the baseline, to cluster patients into two groups at AD diagnosis. We then prognosticated the risk of two readily ascertainable and clinically relevant AD-related outcomes ( Results: We stratified 16,411 algorithm-identified AD patients into two groups based on their baseline clinical profiles (41% Group 1, 59% Group 2). Patients in Group 1 were marginally older at AD diagnosis (age Mean [SD]: 81.4 [9.3] vs 81.0 [8.7], Conclusions: It is feasible to stratify patients based on readily available clinical profiles before AD diagnosis and crucially to update the model one year after diagnosis to effectively prognosticate future AD-related outcomes. SHORT ABSTRACT: Prognostication for people with Alzheimer's disease (AD) at the point of care could improve clinical management. Applying a novel unsupervised latent factor clustering approach guided by knowledge graph embeddings of relevant clinical features from electronic health records, we stratified 16,411 AD patients into two groups at diagnosis and prognosticated their risk of AD-related outcomes (
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