Self-Supervised Temporal Pattern Mining for precision oncology clinical workflows across multilingual stakeholder groups

In early 2024, the author discovered significant asymmetry in clinical data flow across oncology workflows, characterized by temporal and linguistic mismatches. This insight led to a deep dive into self-supervised temporal pattern mining for precision oncology, focusing on understanding actual clinical workflow functions.