K-STAMM: a knowledge-enhanced spatial – temporal attention model with multimodal fusion for pneumonia prediction

Abstract Precise prediction of pneumonia remains a challenge mainly because effective integration of clinical data that are highly heterogeneous is mandatory. The types of clinical data in question include longitudinal electronic health records (EHRs), medical imaging, clinical text, and domain know...

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Autores principales:	S. Anbukkarasi, S. Hemalatha, Arunkumar Balakrishnan, S. Varadhaganapathy, Sathishkumar Veerappampalayam Easwaramoorthy
Formato:	Artigo
Lenguaje:	Inglês
Publicado:	Nature Portfolio 2026-04-01
Colección:	Scientific Reports
Materias:	Multimodal deep learning Electronic health records (EHR) Medical image analysis Clinical risk prediction Knowledge-enhanced attention
Acceso en línea:	https://doi.org/10.1038/s41598-026-47146-w
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