Search Articles

View query in Help articles search

Search Results (1 to 10 of 2396 Results)

Download search results: CSV END BibTex RIS


Unsupervised Deep Learning of Electronic Health Records to Characterize Heterogeneity Across Alzheimer Disease and Related Dementias: Cross-Sectional Study

Unsupervised Deep Learning of Electronic Health Records to Characterize Heterogeneity Across Alzheimer Disease and Related Dementias: Cross-Sectional Study

From the row-wise entropy, this vector is softmaxed to obtain the corresponding weights, w ∈ Rn×1, as follows: The resultant embedding for a note sequence, N ∈ R768×1, is then given by the matrix multiplication as follows: and the final patient-level representation is the simple average over all note fragments for a given patient, for their most recent encounter.

Matthew West, You Cheng, Yingnan He, Yu Leng, Colin Magdamo, Bradley T Hyman, John R Dickson, Alberto Serrano-Pozo, Deborah Blacker, Sudeshna Das

JMIR Aging 2025;8:e65178