Leveraging Pre-trained and Transformer-derived Embeddings from EHRs to Characterize Heterogeneity Across Alzheimer's Disease and Related Dementias
<|reference_start|>Leveraging Pre-trained and Transformer-derived Embeddings from EHRs to Characterize Heterogeneity Across Alzheimer's Disease and Related Dementias: Alzheimer's disease is a progressive, debilitating neurodegenerative disease that affects 50 million people globally. Despite this substantial health burden, available treatments for the disease are limited and its fundamental causes remain poorly understood. Previous work has suggested the existence of clinically-meaningful sub-types, which it is suggested may correspond to distinct etiologies, disease courses, and ultimately appropriate treatments. Here, we use unsupervised learning techniques on electronic health records (EHRs) from a cohort of memory disorder patients to characterise heterogeneity in this disease population. Pre-trained embeddings for medical codes as well as transformer-derived Clinical BERT embeddings of free text are used to encode patient EHRs. We identify the existence of sub-populations on the basis of comorbidities and shared textual features, and discuss their clinical significance.<|reference_end|>
@article{west2024leveraging,
    title={Leveraging Pre-trained and Transformer-derived Embeddings from EHRs to
  Characterize Heterogeneity Across Alzheimer's Disease and Related Dementias},
    author={Matthew West, Colin Magdamo, Lily Cheng, Yingnan He, Sudeshna Das},
    journal={arXiv preprint arXiv:2404.00464},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.00464},
    primaryClass={cs.LG}
}