Discovering patient groups in sequential electronic healthcare data using unsupervised representation learning

Discovering patient groups in sequential electronic healthcare data using unsupervised representation learning

Abstract Introduction Unsupervised feature learning methods inspired by natural language processing (NLP) models are capable of constructing patient-specific features from longitudinal Electronic Health Records (EHR). Design We applied document embedding algorithms to real-world paediatric intensive...

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Main Authors: Jingteng Li, Kimberley R. Zakka, John Booth, Louise Rigny, Samiran Ray, Mario Cortina-Borja, Payam Barnaghi, Neil Sebire
Format: Article
Language:English
Published: BMC 2025-01-01
Series:BMC Medical Informatics and Decision Making
Online Access:https://doi.org/10.1186/s12911-024-02812-9
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