On Entity Embeddings for Ordinal Features as Representation Learning in Recurrence Prediction of Urothelial Bladder Cancer.
Journal:
Studies in health technology and informatics
PMID:
39176889
Abstract
BACKGROUND: Urothelial Bladder Cancer (UBC) is a common cancer with a high risk of recurrence, which is influenced by the TNM classification, grading, age, and other factors. Recent studies demonstrate reliable and accurate recurrence prediction using Machine Learning (ML) algorithms and even outperform traditional approaches. However, most ML algorithms cannot process categorical input features, which must first be encoded into numerical values. Choosing the appropriate encoding strategy has a significant impact on the prediction quality.