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408P Combining rules and machine learning to improve sustainability and explicability of the extraction of pathological cancer markers from patient reports: Cross-sectional multicenter cohort study
0
Zitationen
9
Autoren
2025
Jahr
Abstract
Machine learning (ML) information extraction (IE) algorithms are associated with a concerning carbon footprint and lack explicability. We aimed to compare the performance of 2 IE models based on ML and rules, respectively, and to develop an IE method combining both approaches to optimize performance, sustainability and explicability.
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