An explainable transformer model learning from entire treatment timelines for pan-cancer risk profiling across healthcare systems
Philipp Keyl, Niklas Kiermeyer, Jonah Bosserhoff, Tim Lenfers et autres
Abstract Cancer outcomes vary widely between individual patients, each accumulating an irregular record of treatments, diagnoses, measurements, and complications. Current prognostic models reduce this complexity into a single snapshot, focus on narrow clinical settings, and rarely generalize across hospitals. Here we introduce …
de, ch, ca, gr, us, kr (code pays fourni par la source)
0 citations
medRxiv