A Scalable Deep Learning Analytics Pipeline for Converting Longitudinal Real-World Data Into Predictive Disease Trajectories

Real-world healthcare data encode longitudinal patterns reflecting evolving patient trajectories, but these patterns are fragmented, irregular, and sparse. Static classifiers and heuristic trigger systems rely on explicit diagnosis codes or narrow event heuristics, limiting their ability to detect e...

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Autor principal:	Appala Venkata Subhadra Raju Dantuluri
Format:	Artigo
Idioma:	Inglês
Publicat:	IEEE 2026-01-01
Col·lecció:	IEEE Access
Matèries:	Deep learning temporal representation learning recurrent neural networks healthcare analytics electronic health records longitudinal clinical data
Accés en línia:	https://ieeexplore.ieee.org/document/11392806/
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